{"title":"基于注意力的混合胶囊网络集成三通路网络视网膜图像慢性疾病检测","authors":"M. Mohamed Yaseen, Thulasi Bai Vijayan","doi":"10.1111/jep.70126","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Over the past 20 years, researchers have concentrated on generating retinal images as a means of detecting and classifying chronic diseases. Early diagnosis and treatment are essential to avoid chronic diseases. Manually grading retinal images is time-consuming, prone to errors, and lacks patient-friendliness. Various Deep Learning (DL) algorithms are employed to detect chronic diseases from retinal fundus images. Also, these methods have some disadvantages, such as overfitting, computational cost, and so on.</p>\n </section>\n \n <section>\n \n <h3> Objective</h3>\n \n <p>The proposed research aims to develop Optimized DL based system for detecting chronic diseases in retinal images and solving existing issues.</p>\n </section>\n \n <section>\n \n <h3> Methodology</h3>\n \n <p>Initially, the retinal images are pre-processed to clean and organize the data. Normalization and HSI Colour Conversion are the techniques used for pre-processing. Inception-V3, ResNet-152 and a Convolutional Vision Transformer (Conv-ViT) are used to perform feature extraction. The classifier is an Optimized Hybrid Attention-based Capsule Network. An optimization is included in the proposed model to increase the classifier s performance.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The proposed approach attains accuracies of 99.05 % and 99.15% using Diabetic Retinopathy 224 × 224 (2019 Data) and the APTOS-2019 dataset, respectively. The superior performance of the proposed technique highlights its effectiveness in this domain.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The implementation of such automated methods can significantly improve the efficiency and accuracy of chronic disease diagnosis, benefiting both healthcare providers and patients.</p>\n </section>\n </div>","PeriodicalId":15997,"journal":{"name":"Journal of evaluation in clinical practice","volume":"31 4","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimised Hybrid Attention-Based Capsule Network Integrated Three-Pathway Network for Chronic Disease Detection in Retinal Images\",\"authors\":\"M. Mohamed Yaseen, Thulasi Bai Vijayan\",\"doi\":\"10.1111/jep.70126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Over the past 20 years, researchers have concentrated on generating retinal images as a means of detecting and classifying chronic diseases. Early diagnosis and treatment are essential to avoid chronic diseases. Manually grading retinal images is time-consuming, prone to errors, and lacks patient-friendliness. Various Deep Learning (DL) algorithms are employed to detect chronic diseases from retinal fundus images. Also, these methods have some disadvantages, such as overfitting, computational cost, and so on.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>The proposed research aims to develop Optimized DL based system for detecting chronic diseases in retinal images and solving existing issues.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methodology</h3>\\n \\n <p>Initially, the retinal images are pre-processed to clean and organize the data. Normalization and HSI Colour Conversion are the techniques used for pre-processing. Inception-V3, ResNet-152 and a Convolutional Vision Transformer (Conv-ViT) are used to perform feature extraction. The classifier is an Optimized Hybrid Attention-based Capsule Network. An optimization is included in the proposed model to increase the classifier s performance.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The proposed approach attains accuracies of 99.05 % and 99.15% using Diabetic Retinopathy 224 × 224 (2019 Data) and the APTOS-2019 dataset, respectively. The superior performance of the proposed technique highlights its effectiveness in this domain.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>The implementation of such automated methods can significantly improve the efficiency and accuracy of chronic disease diagnosis, benefiting both healthcare providers and patients.</p>\\n </section>\\n </div>\",\"PeriodicalId\":15997,\"journal\":{\"name\":\"Journal of evaluation in clinical practice\",\"volume\":\"31 4\",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of evaluation in clinical practice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jep.70126\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of evaluation in clinical practice","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jep.70126","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Optimised Hybrid Attention-Based Capsule Network Integrated Three-Pathway Network for Chronic Disease Detection in Retinal Images
Background
Over the past 20 years, researchers have concentrated on generating retinal images as a means of detecting and classifying chronic diseases. Early diagnosis and treatment are essential to avoid chronic diseases. Manually grading retinal images is time-consuming, prone to errors, and lacks patient-friendliness. Various Deep Learning (DL) algorithms are employed to detect chronic diseases from retinal fundus images. Also, these methods have some disadvantages, such as overfitting, computational cost, and so on.
Objective
The proposed research aims to develop Optimized DL based system for detecting chronic diseases in retinal images and solving existing issues.
Methodology
Initially, the retinal images are pre-processed to clean and organize the data. Normalization and HSI Colour Conversion are the techniques used for pre-processing. Inception-V3, ResNet-152 and a Convolutional Vision Transformer (Conv-ViT) are used to perform feature extraction. The classifier is an Optimized Hybrid Attention-based Capsule Network. An optimization is included in the proposed model to increase the classifier s performance.
Results
The proposed approach attains accuracies of 99.05 % and 99.15% using Diabetic Retinopathy 224 × 224 (2019 Data) and the APTOS-2019 dataset, respectively. The superior performance of the proposed technique highlights its effectiveness in this domain.
Conclusion
The implementation of such automated methods can significantly improve the efficiency and accuracy of chronic disease diagnosis, benefiting both healthcare providers and patients.
期刊介绍:
The Journal of Evaluation in Clinical Practice aims to promote the evaluation and development of clinical practice across medicine, nursing and the allied health professions. All aspects of health services research and public health policy analysis and debate are of interest to the Journal whether studied from a population-based or individual patient-centred perspective. Of particular interest to the Journal are submissions on all aspects of clinical effectiveness and efficiency including evidence-based medicine, clinical practice guidelines, clinical decision making, clinical services organisation, implementation and delivery, health economic evaluation, health process and outcome measurement and new or improved methods (conceptual and statistical) for systematic inquiry into clinical practice. Papers may take a classical quantitative or qualitative approach to investigation (or may utilise both techniques) or may take the form of learned essays, structured/systematic reviews and critiques.