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A Hybrid Deep Learning Multi-Class Classification Model for Alzheimer’s Disease Using Enhanced MRI Images 认领 引用
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作者 Ghadah Naif Alwakid 《Computers, Materials & Continua》 SCIE EI 2026年第1期797-821,共25页
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru... Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice. 展开更多
关键词 Alzheimer’s disease deep learning MRI images MobileNetV2 contrast-limited adaptive histogram equalization(CLAHE) enhanced super-resolution generative adversarial networks(ESRGAN) multi-class classification
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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate 认领 引用
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作者 Suganya Athisayamani A.Robert Singh +1 位作者 Gyanendra Prasad Joshi Woong Cho 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期155-183,共29页
In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue... In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%. 展开更多
关键词 MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer
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Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace 认领 引用 被引量：17
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作者 LIU Li-mei WANG An-na +1 位作者 SHA Mo ZHAO Feng-yun 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2011年第10期17-23,33,共7页
Aiming at the limitations of rapid fault diagnosis of blast furnace,a novel strategy based on cost-conscious least squares support vector machine(LS-SVM)is proposed to solve this problem.Firstly,modified discrete part... Aiming at the limitations of rapid fault diagnosis of blast furnace,a novel strategy based on cost-conscious least squares support vector machine(LS-SVM)is proposed to solve this problem.Firstly,modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters.Secondly,cost-con-scious formula is presented for fitness function and it contains in detail training time,recognition accuracy and the feature selection.The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier.The new method can select the best fault features in much shorter time and have fewer support vectbrs and better general-ization performance in the application of fault diagnosis of the blast furnace.Thirdly,a gradual change binary tree is established for blast furnace faults diagnosis.It is a multi-class classification method based on center-of-gravity formula distance of cluster.A gradual change classification percentage ia used to select sample randomly.The proposed new metbod raises the sped of diagnosis,optimizes the classifieation scraraey and has good generalization ability for fault diagnosis of the application of blast furnace. 展开更多
关键词 blast furnace fault diagnosis eosc-conscious LS-SVM multi-class classification
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Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects 认领 引用 被引量：6
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作者 Mao-xiang CHU An-na WANG +1 位作者 Rong-fen GONG Mo SHA 《Journal of Iron and Steel Research International》 SCIE CAS CSCD 2014年第2期174-180,共7页
Considering strip steel surface defect samples, a multi-class classification method was proposed based on enhanced least squares twin support vector machines （ELS-TWSVMs） and binary tree. Firstly, pruning region sam... Considering strip steel surface defect samples, a multi-class classification method was proposed based on enhanced least squares twin support vector machines （ELS-TWSVMs） and binary tree. Firstly, pruning region samples center method with adjustable pruning scale was used to prune data samples. This method could reduce classifierr s training time and testing time. Secondly, ELS-TWSVM was proposed to classify the data samples. By introducing error variable contribution parameter and weight parameter, ELS-TWSVM could restrain the impact of noise sam- ples and have better classification accuracy. Finally, multi-class classification algorithms of ELS-TWSVM were pro- posed by combining ELS-TWSVM and complete binary tree. Some experiments were made on two-dimensional data- sets and strip steel surface defect datasets. The experiments showed that the multi-class classification methods of ELS-TWSVM had higher classification speed and accuracy for the datasets with large-scale, unbalanced and noise samples. 展开更多
关键词 multi-class classification least squares twin support vector machine error variable contribution weight binary tree strip steel surface
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Multi-class classification method for steel surface defects with feature noise 认领 引用 被引量：2
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作者 Mao-xiang Chu Yao Feng +1 位作者 Yong-hui Yang Xin Deng 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2021年第3期303-315,共13页
Defect classification is the key task of a steel surface defect detection system.The current defect classification algorithms have not taken the feature noise into consideration.In order to reduce the adverse impact o... Defect classification is the key task of a steel surface defect detection system.The current defect classification algorithms have not taken the feature noise into consideration.In order to reduce the adverse impact of feature noise,an anti-noise multi-class classification method was proposed for steel surface defects.On the one hand,a novel anti-noise support vector hyper-spheres(ASVHs)classifier was formulated.For N types of defects,the ASVHs classifier built N hyper-spheres.These hyper-spheres were insensitive to feature and label noise.On the other hand,in order to reduce the costs of online time and storage space,the defect samples were pruned by support vector data description with parameter iteration adjustment strategy.In the end,the ASVHs classifier was built with sparse defect samples set and auxiliary information.Experimental results show that the novel multi-class classification method has high efficiency and accuracy for corrupted defect samples in steel surface. 展开更多
关键词 Steel surface defect Multi-class classification Anti-noise support vector hyper-sphere Parameter iteration adjustment Feature noise
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Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere 认领 引用 被引量：2
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作者 Mao-xiang Chu Xiao-ping Liu +1 位作者 Rong-fen Gong Jie Zhao 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2018年第7期706-716,共11页
Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated f... Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere （AHSVM） is formulated. Meanwhile, a new multi-class classification method is proposed. Originated from support vector data description, AHSVM adopts hyper-sphere to solve classification problem. AHSVM can obey two principles： the margin maximization and inner-class dispersion minimization. Moreover, the hyper-sphere of AHSVM is adjustable, which makes the final classification hyper-sphere optimal for training dataset. On the other hand, AHSVM is combined with binary tree to solve multi-class classification for steel surface defects. A scheme of samples pruning in mapped feature space is provided, which can reduce the number of training samples under the premise of classification accuracy, resulting in the improvements of classification speed. Finally, some testing experiments are done for eight types of strip steel surface defects. Experimental results show that multi-class AHSVM classifier exhibits satisfactory results in classification accuracy and efficiency. 展开更多
关键词 Strip steel surface defect Multi-class classification Supporting vector machine Adjustable hyper-sphere
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A combined algorithm of K-means and MTRL for multi-class classification 认领 引用 被引量：2
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作者 XUE Mengfan HAN Lei PENG Dongliang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2019年第5期875-885,共11页
The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class cla... The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs.Rest to disassemble the multi-class classification task into binary classification tasks.K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs.Finally,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained together.With the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary classifier.The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset. 展开更多
关键词 machine learning multi-class classification K-means multi-task relationship learning (MTRL) over-fitting
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Decision tree support vector machine based on genetic algorithm for multi-class classification 认领 引用 被引量：17
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作者 Huanhuan Chen Qiang Wang Yi Shen 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第2期322-326,共5页
To solve the multi-class fault diagnosis tasks,decision tree support vector machine(DTSVM),which combines SVM and decision tree using the concept of dichotomy,is proposed.Since the classification performance of DTSVM ... To solve the multi-class fault diagnosis tasks,decision tree support vector machine(DTSVM),which combines SVM and decision tree using the concept of dichotomy,is proposed.Since the classification performance of DTSVM highly depends on its structure,to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes,genetic algorithm is introduced into the formation of decision tree,so that the most separable classes would be separated at each node of decisions tree.Numerical simulations conducted on three datasets compared with"one-against-all"and"one-against-one"demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods. 展开更多
关键词 support vector machine(SVM) decision tree geneticalgorithm classification.
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Detection of Angioectasias and Haemorrhages Incorporated into a Multi-Class Classification Tool for the GI Tract Anomalies by Using Binary CNNs 认领 引用
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作者 Christos Barbagiannis Alexios Polydorou +2 位作者 Michail Zervakis Andreas Polydorou Eleftheria Sergaki 《Journal of Biomedical Science and Engineering》 2021年第12期402-414,共13页
The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landm... The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam(R) Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm versus our same goal algorithm based on HSV colorimetric lesions features extracted of pixel-level annotations, both algorithms trained and tested on the same data. It is evaluated that the CNN trained on image level annotated images, is 9% less sensitive, achieves 2.6% less precision, 1.2% less FPR, and 7% less FNR, than that based on HSV filters, extracted from on pixel-level annotated training data. 展开更多
关键词 Capsule Endoscopy (CE) Small Bowel Bleeding (SBB) Angioectasia Haemorrhage Gatrointestinal (GI) Small Bowel Capsule Endoscopy (SBCE) Convolutional Neural Network (CNN) Computer Aided Diagnosis (CAD) Image Level Annotation Pixel Level Annotation Binary Classification
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An Efficient Feature Selection with an Enhanced Supervised Term-Weighting Scheme in Multi-Class Text Classification 认领 引用
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作者 Osamah Mohammed Alyasiri Yu-N Cheah 《Computers, Materials & Continua》 SCIE EI 2026年第6期2490-2513,共24页
Term weighting scheme and feature selection are two fundamental components in text classification(TC)systems,particularly in high-dimensional,multi-class,and imbalanced settings.Term weighting schemes aim to improve d... Term weighting scheme and feature selection are two fundamental components in text classification(TC)systems,particularly in high-dimensional,multi-class,and imbalanced settings.Term weighting schemes aim to improve document representation by emphasizing discriminative terms across classes,while feature selection(FS)seeks to reduce dimensionality,eliminate irrelevant and redundant features,and enhance classification efficiency and effectiveness.However,most existing studies focus on FS independently of the term-weighting strategy used during document representation,thereby limiting the potential benefits of their interaction.This study addresses this gap by pursuing two main objectives.First,it employs an enhanced supervised term-weighting scheme,namely MTF-MICF,to construct a more stable and class-discriminative document representation,especially for imbalanced data.Second,it investigates the effectiveness of integrating this scheme with a filter-based FS approach using Information Gain(IG)at various levels of dimensionality reduction to assess the contribution of enhanced term weighting to the FS process.Extensive experiments were conducted across 19 benchmark multi-class text datasets.The performance was evaluated using F1-score and classification accuracy with the three prominent classifiers(MNB,SVM,and LR).The experimental results demonstrate that the proposed approach consistently outperforms conventional methods,achieving significant and stable improvements in both representation quality and classification performance.These findings confirm that enhanced supervised term weighting can serve as an effective supporting mechanism for FS in high-dimensional TC tasks. 展开更多
关键词 Text classification text categorization feature selection term weighting scheme supervised term-weighting scheme filter feature selection
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A Lightweight YOLOvll Framework for Multi-Class Retinal Disease Classification 认领 引用
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作者 Jaffar Hussain Tahira Nazir +1 位作者 Junaid Rashid Jungeun Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1288-1323,共36页
Early detection of diabetic retinopathy(DR),media haze(MH),optic disc cupping(ODC),and glaucoma is crucial for preventing vision loss.However,timely diagnosis is often constrained by limited specialist availability an... Early detection of diabetic retinopathy(DR),media haze(MH),optic disc cupping(ODC),and glaucoma is crucial for preventing vision loss.However,timely diagnosis is often constrained by limited specialist availability and high diagnostic costs.This study proposes a You Only Look Once(YOLO)-based deep learning(DL)framework for the automated classification of fundus images into disease-specific categories.We unified diverse annotations from the Retinal Fundus Multi-Disease image Dataset(RFMiD),RFMiD2.0,and the DR Fundus Image Dataset(DR-FID)by standardizing annotation files and class labels.A custom filtering module was used to isolate singlepathology cases,and dataset issues such as missing or corrupted files were identified and resolved.To handle class imbalance,we applied oversampling and undersampling methods.The dataset was re-engineered for lightweight accurate classification with YOLOvll,utilizing offline preprocessing tailored for retinal images.The dataset design leverages YOLOvll's multi-class classification framework to achieve high performance on resource-constrained devices.This tailored approach outperforms preparing datasets solely through cloud-based platforms like Roboflow.The proposed model uses a lightweight YOLOvll architecture,resulting in faster inference and lower memory requirements than conventional Convolutional Neural Networks(CNNs),such as Residual Networks(ResNets)or Visual Geometry Group(VGG)networks.Delivering high accuracy with minimal resource use,the model shows no signs of divergence or overfitting.Confusion matrices and class-wise metrics confirm consistent performance.The proposed framework achieves improved performance,with 94.78%accuracy,96.12%specificity,79.61%precision,83.61%recall,and an 81.14%Fl-score,demonstrating strong generalization to the internal held-out test set. 展开更多
关键词 Image classification fundus convolutional neural networks deep learning machine learning
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NeuroTriad-ViT:A Scalable and Interpretable Framework for Multi-Class Brain Tumor Classification via MRI and Knowledge Distillation 认领 引用
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作者 Sultan Kahla Zuping Zhang +3 位作者 Majed Alsafyani Ahmed Emara Mohammod Abdullah Bin Hossain Abdulwahab Osman Sheikhdon 《Computers, Materials & Continua》 SCIE EI 2026年第6期1817-1841,共25页
The effective diagnosis and treatment planning require the correct classification of the cerebral neoplasia,such as glioma,meningioma,and pituitary tumors.The recent developments in the deep learning field have made a... The effective diagnosis and treatment planning require the correct classification of the cerebral neoplasia,such as glioma,meningioma,and pituitary tumors.The recent developments in the deep learning field have made a significant contribution to the field of image analysis in medicine;however,Vision Transformers(ViTs)have achieved good results but are computationally complex.This paper presents NeuroTriad-ViT,a proprietary large-scale Vision Transformer of 235 million parameters,which is represented as a high-performance teacher model to classify brain tumors.Knowledge distillation is applied in an attempt to transfer the representations that the teacher learned to lightweight student models,in which MobileNetV2 outperformed EfficientNet0-Lite.The models were conditioned on 24,455 MRI scans,which were combined from three publicly available datasets.The CNN-pretrained ViT-based hybrid architecture was the highest-accuracy heterogeneous hybrid model reported as teacher-nominated,with 96%accuracy,and the CNN-architecture-based ensemble models(using EfficientNet,VGG16,and DenseNet)had a maximum accuracy of 92%-95%.Comparatively,the NeuroTriad-ViT model had an accuracy of 98%and the distilled MobileNetV2 model had an accuracy of 99.32%,thus it can be seen as having better performance with lower computational cost.The interpretability of models was tested based on Grad-CAM and LIME,and the measures of insertion and deletion support the faithfulness of the explanations.Overall,the proposed framework enables efficient,interpretable,and scalable brain tumor diagnosis suitable for real-time clinical and mobile health deployment.The source code is publicly available at http://gffzzd3cc09b8251d45dfs596up5fok9cn6q0v.ffgz.tsg.suse.edu.cn/10.5281/zenodo.17494928. 展开更多
关键词 Vision transformer CNN feature fusion knowledge distillation medical image classification ensemble learning explainable AI(XAI)
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Enhancing Multi-Class Cyberbullying Classification with Hybrid Feature Extraction and Transformer-Based Models 认领 引用 被引量：1
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作者 Suliman Mohamed Fati Mohammed A.Mahdi +4 位作者 Mohamed A.G.Hazber Shahanawaj Ahamad Sawsan A.Saad Mohammed Gamal Ragab Mohammed Al-Shalabi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第5期2109-2131,共23页
Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or... Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content. 展开更多
关键词 Cyberbullying classification multi-class classification BERT models machine learning TF-IDF Word2Vec social media analysis transformer models
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Multimodal Signal Processing of ECG Signals with Time-Frequency Representations for Arrhythmia Classification 认领 引用 被引量：1
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作者 Yu Zhou Jiawei Tian Kyungtae Kang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期990-1017,共28页
Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin... Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification. 展开更多
关键词 Electrocardiogram arrhythmia classification multimodal time-frequency representation
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Review of the classification and related terminology of fine-grained sedimentary rocks 认领 引用 被引量：1
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作者 ZHU Rukai SUN Longde +2 位作者 ZOU Caineng CHEN Yang MIAO Xue 《Petroleum Exploration and Development》 SCIE 2026年第1期61-78,共18页
Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a class... Through tracing the background and customary usage of classification of fine-grained sedimentary rocks and terminology,and comparing current“sedimentary petrology”textbooks and monographs,this paper proposes a classification scheme for fine-grained sedimentary rocks and clarifies related terminology.The comprehensive analysis indicates that the classification of clastic rocks,volcanic clastic rocks,chemical rocks,and biogenic(carbonate)rocks is unified,and the definitions of terms such as lamination,bedding and beds are consistent.However,there is a disagreement on the definition of“mud”.European and American scholars commonly use the term“mud”to include silt and clay(particle size less than 0.0625 mm).Chinese scholars equate the term“mud”to“clay”(particle size less than 0.0039 mm or less than 0.01 mm).Combined with the discussion on terms such as sedimentary structures(bedding,lamination and lamellation),shale,mudstone,mudrocks/argillaceous rocks and mud shale,it is recommended to use“fine-grained sedimentary rocks”as the general term for all sedimentary rocks composed of fine-grained materials with particle size less than 0.0625 mm,including claystone/mudrocks and siltstone.Claystone/mudrocks are further classified into argillaceous(or clayey)mudstone/shale,calcareous mudstone/shale,siliceous mudstone/shale,silty mudstone/shale and silt-containing mudstone/shale.Argillaceous(or clayey)mudstone/shale emphasizes a content of clay minerals or clay-sized particles exceeding 50%.Other mudstones/shales emphasize a content of particles(particle size less than 0.0625 mm)exceeding 50%.The commonly referred term“shale”should not include siltstone.It is necessary to establish a reasonable,standardized,and applicable classification scheme for fine-grained sedimentary rocks in the future.An integrated shale microfacies research at the thin-section scale should be carried out,and combined with well logging data interpretation and seismic attribute analysis,a geological model of lithology/lithofacies will be iteratively upgraded to accurately determine sweet layer,locate target layer,and evaluate favorable area. 展开更多
关键词 fine-grained sedimentary rock shale mudstone clay shale oil shale gas lamellation shale microfacies classification scheme fine-grained sedimentology
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Application of a novel adverse event classification scale in a Latin American gastrointestinal endoscopy unit 认领 引用 被引量：1
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作者 Oscar Corsi Richard Martinez +7 位作者 Joaquín Aguirre Isidora Friedrich Victoria Galeno Vicente Jimenez Pamela Briones Luis Antonio Díaz Alberto Espino Jose Ignacio Vargas 《World Journal of Gastrointestinal Endoscopy》 2026年第1期87-94,共8页
BACKGROUND Accurate classification of adverse events(AEs)in gastrointestinal endoscopy is essential for safety monitoring and quality improvement.The American Society for Gastrointestinal Endoscopy(ASGE)lexicon is wid... BACKGROUND Accurate classification of adverse events(AEs)in gastrointestinal endoscopy is essential for safety monitoring and quality improvement.The American Society for Gastrointestinal Endoscopy(ASGE)lexicon is widely used,while the classification for AEs in gastrointestinal endoscopy(AGREE)is a recently proposed alternative aiming for broader applicability.AIM To compare the agreement and correlation between the AGREE and ASGE classification systems using real-world data from a Latin American academic endoscopy unit.METHODS A retrospective analysis of a prospective registry was conducted at a tertiary center in Chile,encompassing all endoscopy-related AEs from 2009 to 2022.Each AE was independently graded using both ASGE and AGREE classification systems by two blinded reviewers per system.Interobserver agreement was calculated using Cohen’s Kappa,and inter-scale correlation was assessed using Spearman’s rank test.RESULTS Of 176655 procedures performed,235 AEs(0.13%)were included.Most events were related to therapeutic procedures,and the most common AEs were cardiorespiratory(42.1%),bleeding(20.9%),and perforation(17.0%).The ASGE system identified 42.1%of cases as incidents and 57.9%as AEs(Kappa=0.83).AGREE classified 46.0%as non-AEs and 54.0%as AEs(Kappa=0.74).A strong correlation between both systems was observed(ρ=0.89;P<0.001).CONCLUSION The AGREE classification strongly correlates with the ASGE lexicon but excludes more cases as non-AEs and shows slightly lower interobserver agreement.These findings support AGREE as a feasible alternative for AE grading in gastrointestinal endoscopy,particularly in diverse clinical environments. 展开更多
关键词 Gastrointestinal endoscopy Adverse events classification Adverse events in gastrointestinal endoscopy American Society for Gastrointestinal Endoscopy Patient safety Latin America
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CANNSkin:A Convolutional Autoencoder Neural Network-Based Model for Skin Cancer Classification 认领 引用
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作者 Abdul Jabbar Siddiqui Saheed Ademola Bello +3 位作者 Muhammad Liman Gambo Abdul Khader Jilani Saudagar Mohamad A.Alawad Amir Hussain 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1142-1165,共24页
Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities,variations in skin texture,the presence of hair,and inconsistent illumination.Deep learning models have shown promise in assisting ... Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities,variations in skin texture,the presence of hair,and inconsistent illumination.Deep learning models have shown promise in assisting early detection,yet their performance is often limited by the severe class imbalance present in dermoscopic datasets.This paper proposes CANNSkin,a skin cancer classification framework that integrates a convolutional autoencoder with latent-space oversampling to address this imbalance.The autoencoder is trained to reconstruct lesion images,and its latent embeddings are used as features for classification.To enhance minority-class representation,the Synthetic Minority Oversampling Technique(SMOTE)is applied directly to the latent vectors before classifier training.The encoder and classifier are first trained independently and later fine-tuned end-to-end.On the HAM10000 dataset,CANNSkin achieves an accuracy of 93.01%,a macro-F1 of 88.54%,and an ROC–AUC of 98.44%,demonstrating strong robustness across ten test subsets.Evaluation on the more complex ISIC 2019 dataset further confirms the model’s effectiveness,where CANNSkin achieves 94.27%accuracy,93.95%precision,94.09%recall,and 99.02%F1-score,supported by high reconstruction fidelity(PSNR 35.03 dB,SSIM 0.86).These results demonstrate the effectiveness of our proposed latent-space balancing and fine-tuned representation learning as a new benchmark method for robust and accurate skin cancer classification across heterogeneous datasets. 展开更多
关键词 Computational image processing imbalance classification medical image analysis melanoma skin cancer classification
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Enhancing multiclass brain tumor classification through automated segmentation-guided deep learning 认领 引用
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作者 Pattaramon Vuttipittayamongkol Phakorn Charoenthiphakorn +2 位作者 Yarida Fuangfoo Pornnapha Na Phirot Thanawat Sanosiang 《Medical Data Mining》 CAS 2026年第2期15-33,共19页
Background:Accurate classification of brain tumors from Magnetic Resonance Imaging(MRI)is essential for clinical decision-making but remains challenging due to tumor heterogeneity.Existing approaches often focus solel... Background:Accurate classification of brain tumors from Magnetic Resonance Imaging(MRI)is essential for clinical decision-making but remains challenging due to tumor heterogeneity.Existing approaches often focus solely on classification or treat segmentation and classification as separate tasks,limiting overall performance and interpretability.Methods:This study proposes an end-to-end automated framework that integrates optimized tumor localization with multiclass classification.An optimized segmentation model is first employed to generate tumor masks,which are then overlaid on MRI scans to produce attention-enhanced inputs.These inputs are subsequently used to train a convolutional neural network(CNN)classifier.Experiments were conducted on a public dataset comprising 4,237 MRI scans across four categories:normal,glioma,meningioma,and pituitary tumors.Results:Three widely used segmentation models were systematically evaluated,with an optimized U-Net achieving the best performance(accuracy=0.9939,Dice=0.8893).Segmentation-guided classification consistently improved performance across six CNN architectures,with the most notable gains observed in heterogeneous tumor types such as glioma and meningioma.Among the classifiers,EfficientNet-V2 achieved the highest performance,with an accuracy of 0.9835,precision of 0.9858,recall of 0.9804,and F1-score of 0.9828.The framework was further validated on an independent external dataset,demonstrating consistent performance and robustness across diverse MRI sources.Conclusion:The proposed framework demonstrates strong potential for multiclass brain tumor classification by effectively combining segmentation and classification.This segmentation-driven approach not only enhances predictive accuracy but also improves interpretability,making it more suitable for clinical applications. 展开更多
关键词 brain tumor classification MRI segmentation segmentation-guided CNN multiclass classification tumor localization medical imaging
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A robust phylogenomic framework supports a revised intrafamilial classification of Urticaceae 认领 引用
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作者 Xiao-Gang Fu Jie Liu +8 位作者 Richard I.Milne Alex K.Monro Shui-Yin Liu Qin Tian Gregory W.Stull Amos Kipkoech Ting-Shuang Yi De-Zhu Li Zeng-Yuan Wu 《Plant Diversity》 SCIE CAS CSCD 2026年第2期289-306,共18页
Over the past decade,phylogenomics has significantly enhanced our understanding of relationships among numerous angiosperm lineages.However,comprehensive phylogenetic studies combining broad sampling of both genomic s... Over the past decade,phylogenomics has significantly enhanced our understanding of relationships among numerous angiosperm lineages.However,comprehensive phylogenetic studies combining broad sampling of both genomic sequences and taxa within the nettle family(Urticaceae)are still lacking.Here,we reconstructed the phylogeny of Urticaceae(345 species across 89% of accepted genera)using concatenated and coalescent analyses from plastome and nuclear ribosomal DNA sequences.Different plastid datasets and tree inference methods yielded a consistent phylogenetic backbone,with 98% of nodes achieving>90% bootstrap support—a significant improvement compared to 54% of nodes in the latest published phylogenetic study of Urticaceae.Plastid and nuclear phylogenetic relationships were largely congruent,with several exceptions that warrant further study.In the context of the updated phylogenetic relationships,we propose dividing the family into seven tribes that correspond to seven major clades or subclades,including a newly established tribe,Sarcochlamydeae stat.nov.Our phylogenetic analysis indicates that Debregeasia and Phenax are non-monophyletic.By combing morphological,molecular and distributional evidence,we describe a new genus Chiajuia gen.nov.Additionally,we propose synonymizing the following genera:Cypholophus(to Boehmeria),Haroldiella(to Pilea),Hemistylus,Neodistemon,Rousselia(all to Pouzolzia),Hesperocnide(to Urtica),and Pellionia(to Elatostema),while recognizing Elatostematoides,Gonostegia,Leptocnide,Margarocarpus,Scepocarpus,and Sceptrocnide as distinct genera.This robust phylogenomic framework and revised classification lays a foundation for future studies on the evolution and ecology of Urticaceae.The approach applied here may also serve as an important reference for other large plant families in angiosperms. 展开更多
关键词 Chiajuia Intrafamilial classification Phylogenomics Plastome Sarcochlamydeae Urticaceae
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