Asghar Amir, Tariqullah Jan, Mohammad Haseeb Zafar, Shadan Khan Khattak
{"title":"医学影像中多标签视网膜疾病分类的复杂集成深度学习方法","authors":"Asghar Amir, Tariqullah Jan, Mohammad Haseeb Zafar, Shadan Khan Khattak","doi":"10.1049/cit2.70012","DOIUrl":null,"url":null,"abstract":"<p>This paper introduces a novel ensemble Deep learning (DL)-based Multi-Label Retinal Disease Classification (MLRDC) system, known for its high accuracy and efficiency. Utilising a stacking ensemble approach, and integrating DenseNet201, EfficientNetB4, EfficientNetB3 and EfficientNetV2S models, exceptional performance in retinal disease classification is achieved. The proposed MLRDC model, leveraging DL as the meta-model, outperforms individual base detectors, with DenseNet201 and EfficientNetV2S achieving an accuracy of 96.5%, precision of 98.6%, recall of 97.1%, and F1 score of 97.8%. Weighted multilabel classifiers in the ensemble exhibit an average accuracy of 90.6%, precision of 98.3%, recall of 91.2%, and F1 score of 94.6%, whereas unweighted models achieve an average accuracy of 90%, precision of 98.6%, recall of 93.1%, and F1 score of 95.7%. Employing Logistic Regression (LR) as the meta-model, the proposed MLRDC system achieves an accuracy of 93.5%, precision of 98.2%, recall of 93.9%, and F1 score of 96%, with a minimal loss of 0.029. These results highlight the superiority of the proposed model over benchmark state-of-the-art ensembles, emphasising its practical applicability in medical image classification.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1159-1173"},"PeriodicalIF":7.3000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70012","citationCount":"0","resultStr":"{\"title\":\"Sophisticated Ensemble Deep Learning Approaches for Multilabel Retinal Disease Classification in Medical Imaging\",\"authors\":\"Asghar Amir, Tariqullah Jan, Mohammad Haseeb Zafar, Shadan Khan Khattak\",\"doi\":\"10.1049/cit2.70012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This paper introduces a novel ensemble Deep learning (DL)-based Multi-Label Retinal Disease Classification (MLRDC) system, known for its high accuracy and efficiency. Utilising a stacking ensemble approach, and integrating DenseNet201, EfficientNetB4, EfficientNetB3 and EfficientNetV2S models, exceptional performance in retinal disease classification is achieved. The proposed MLRDC model, leveraging DL as the meta-model, outperforms individual base detectors, with DenseNet201 and EfficientNetV2S achieving an accuracy of 96.5%, precision of 98.6%, recall of 97.1%, and F1 score of 97.8%. Weighted multilabel classifiers in the ensemble exhibit an average accuracy of 90.6%, precision of 98.3%, recall of 91.2%, and F1 score of 94.6%, whereas unweighted models achieve an average accuracy of 90%, precision of 98.6%, recall of 93.1%, and F1 score of 95.7%. Employing Logistic Regression (LR) as the meta-model, the proposed MLRDC system achieves an accuracy of 93.5%, precision of 98.2%, recall of 93.9%, and F1 score of 96%, with a minimal loss of 0.029. These results highlight the superiority of the proposed model over benchmark state-of-the-art ensembles, emphasising its practical applicability in medical image classification.</p>\",\"PeriodicalId\":46211,\"journal\":{\"name\":\"CAAI Transactions on Intelligence Technology\",\"volume\":\"10 4\",\"pages\":\"1159-1173\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2025-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70012\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CAAI Transactions on Intelligence Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.70012\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CAAI Transactions on Intelligence Technology","FirstCategoryId":"94","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.70012","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Sophisticated Ensemble Deep Learning Approaches for Multilabel Retinal Disease Classification in Medical Imaging
This paper introduces a novel ensemble Deep learning (DL)-based Multi-Label Retinal Disease Classification (MLRDC) system, known for its high accuracy and efficiency. Utilising a stacking ensemble approach, and integrating DenseNet201, EfficientNetB4, EfficientNetB3 and EfficientNetV2S models, exceptional performance in retinal disease classification is achieved. The proposed MLRDC model, leveraging DL as the meta-model, outperforms individual base detectors, with DenseNet201 and EfficientNetV2S achieving an accuracy of 96.5%, precision of 98.6%, recall of 97.1%, and F1 score of 97.8%. Weighted multilabel classifiers in the ensemble exhibit an average accuracy of 90.6%, precision of 98.3%, recall of 91.2%, and F1 score of 94.6%, whereas unweighted models achieve an average accuracy of 90%, precision of 98.6%, recall of 93.1%, and F1 score of 95.7%. Employing Logistic Regression (LR) as the meta-model, the proposed MLRDC system achieves an accuracy of 93.5%, precision of 98.2%, recall of 93.9%, and F1 score of 96%, with a minimal loss of 0.029. These results highlight the superiority of the proposed model over benchmark state-of-the-art ensembles, emphasising its practical applicability in medical image classification.
期刊介绍:
CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.