Ignatowicz Alicja Anna, Marciniak Tomasz, Marciniak Elżbieta
Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, Poland.
Department of Ophthalmology, Chair of Ophthalmology and Optometry, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 60-780 Poznan, Poland.
Sensors (Basel). 2025 Jun 25;25(13):3954. doi: 10.3390/s25133954.
Cataract remains the leading cause of blindness worldwide, and the number of individuals affected by this condition is expected to rise significantly due to global population ageing. Early diagnosis is crucial, as delayed treatment may result in irreversible vision loss. This study explores and presents a mobile application for Android devices designed for the detection of cataracts using deep learning models. The proposed solution utilizes a multi-stage classification approach to analyze ocular images acquired with a slit lamp, sourced from the Nuclear Cataract Database for Biomedical and Machine Learning Applications. The process involves identifying pathological features and assessing the severity of the detected condition, enabling comprehensive characterization of the NC (nuclear cataract) of cataract progression based on the LOCS III scale classification. The evaluation included a range of convolutional neural network architectures, from larger models like VGG16 and ResNet50, to lighter alternatives such as VGG11, ResNet18, MobileNetV2, and EfficientNet-B0. All models demonstrated comparable performance, with classification accuracies exceeding 91-94.5%. The trained models were optimized for mobile deployment, enabling real-time analysis of eye images captured with the device camera or selected from local storage. The presented mobile application, trained and validated on authentic clinician-labeled pictures, represents a significant advancement over existing mobile tools. The preliminary evaluations demonstrated a high accuracy in cataract detection and severity grading. These results confirm the approach is feasible and will serve as the foundation for ongoing development and extensions.
白内障仍然是全球失明的主要原因，由于全球人口老龄化，预计受这种疾病影响的人数将大幅上升。早期诊断至关重要，因为延迟治疗可能导致不可逆转的视力丧失。本研究探索并展示了一款适用于安卓设备的移动应用程序，该程序旨在使用深度学习模型检测白内障。所提出的解决方案采用多阶段分类方法，分析用裂隙灯采集的眼部图像，这些图像来自用于生物医学和机器学习应用的核性白内障数据库。该过程包括识别病理特征和评估检测到的病情严重程度，从而能够根据LOCS III量表分类对白内障进展的核性白内障（NC）进行全面表征。评估包括一系列卷积神经网络架构，从像VGG16和ResNet50这样的大型模型，到像VGG11、ResNet18、MobileNetV2和EfficientNet - B0这样的轻量级模型。所有模型都表现出可比的性能，分类准确率超过91% - 94.5%。经过训练的模型针对移动部署进行了优化，能够对通过设备摄像头拍摄或从本地存储中选择的眼部图像进行实时分析。所展示的移动应用程序在真实的临床医生标记图片上进行了训练和验证，代表了相对于现有移动工具的重大进步。初步评估显示在白内障检测和严重程度分级方面具有很高的准确性。这些结果证实了该方法是可行的，并将作为持续开发和扩展的基础。
