Published Paper Details:

Early detection and classification of cataracts are crucial for timely medical intervention and vision preservation. Traditional diagnostic methods, such as slit-lamp examinations and fundus imaging, rely heavily on ophthalmologists' expertise, making the process subjective, time-consuming, and resource- intensive. To address these limitations, deep learning-based techniques have emerged as powerful tools for automated cataract detection. In this study, we explore the use of the Inception V3 convolutional neural network (CNN) classifier for cataract detection from fundus images. Inception V3, known for its multi-scale feature extraction capabilities, optimized architecture, and high accuracy in image classification tasks, is trained on a dataset of labeled cataract images. The model undergoes transfer learning and fine-tuning to improve performance on medical image datasets. Experimental results demonstrate that Inception V3 achieves high classification accuracy, outperforming traditional machine learning approaches and other CNN architectures. Furthermore, we analyze the effectiveness of ensemble learning, hybrid CNN- LSTM models, and transfer learning techniques to enhance model robustness and generalizability. The integration of transfer learning, ensemble learning, and hybrid architectures has further improved the effectiveness of cataract classification. While challenges remain in terms of data quality, interpretability, and deployment, ongoing advancements in AI and cloud computing hold the potential to make automated cataract screening accessible globally. The study also highlights challenges such as dataset limitations, image quality variations, and the need for explainable AI in medical applications. Future research directions include the integration of attention mechanisms, real-time deployment in mobile screening applications, and collaboration with clinical experts to refine AI- driven diagnostic systems