Paper Details
Abstract
Cataracts, the clouding of the eye’s lens that leads to progressive vision impairment, remain a major global health challenge. Early and accurate diagnosis is critical for preventing disease progression and improving quality of life, yet traditional grading methods depend on ophthalmologists and specialized equipment, which can be costly and inaccessible in many regions. To address this, we proposed a computer-assisted diagnostic framework for cataract detection and severity classification using fundus retinal images. The approach integrated pretrained Inception and Xception networks for feature extraction, principal component analysis for feature fusion and dimensionality reduction, and ensemble classification using CNN, ANN, and MLP models with majority voting across four severity levels: normal, mild, moderate, and severe. Evaluated on 1,600 fundus images (augmented to 3,000) annotated by an expert ophthalmologist, the framework achieved 98.74\% accuracy, outperforming existing methods. These results highlighted its potential as a reliable, affordable, and accessible tool for early cataract screening and diagnosis.