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

IF 2.1 4区 医学 Q3 HEALTH CARE SCIENCES & SERVICES
M. Mohamed Yaseen, Thulasi Bai Vijayan
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引用次数: 0

摘要

在过去的20年里,研究人员一直致力于生成视网膜图像,作为检测和分类慢性疾病的一种手段。早期诊断和治疗对于避免慢性疾病至关重要。手动对视网膜图像进行分级耗时,容易出错,而且对患者不友好。各种深度学习算法被用于从视网膜眼底图像中检测慢性疾病。同时,这些方法也存在过拟合、计算量大等缺点。目的开发基于优化深度学习的视网膜图像慢性疾病检测系统,解决存在的问题。首先,对视网膜图像进行预处理,对数据进行清理和组织。归一化和HSI颜色转换是用于预处理的技术。使用Inception-V3, ResNet-152和卷积视觉变压器(convv - vit)进行特征提取。该分类器是一种优化的基于注意力的混合胶囊网络。为了提高分类器的性能,该模型进行了优化。结果使用糖尿病视网膜病变224 × 224(2019年数据)和APTOS-2019数据集,该方法的准确率分别达到99.05%和99.15%。该技术的优越性能突出了其在该领域的有效性。结论该自动化方法的实施可显著提高慢性疾病诊断的效率和准确性,使医护人员和患者均受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
CiteScore
4.80
自引率
4.20%
发文量
143
审稿时长
3-8 weeks
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.
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