A novel MobileNet based cataract eye disease classification

The major issue faced by the healthcare organizations in real world is that early identification and detection of diseases through the incorporation of artificial intelligence, especially deep learning. Early and accurate detection of cataracts is now possible thanks to new developments in medical imaging and artificial intelligence (AI). Traditional diagnostic approaches for cataracts rely on clinical examinations by professionals. To provide quicker and more reliable diagnoses, deep learning models have demonstrated great potential in automating the examination of retinal and lens images. The deep learning models like MobileNet and MobileNetV2 were used to diagnose diseases using imaging datasets, with a focus on medical picture to categories different eye ailments from medical image datasets with an emphasis on diagnosing eye diseases. The MobileNet algorithm demonstrated superior performance, achieving an accuracy of 97%, compared to MobileNetV2 accuracy of 70%. Thus, the findings of this study advocate for the adoption of MobileNet is the preferred predictive deep learning modelling technique for identifying the early classification of cataract eye disease in humans.