YOLO V8: The AI-Enabled Cataract Detection System

Cataract, a cause of vision loss and blindness worldwide, among older individuals affects almost half of the elderly population in India. Detecting and treating cataracts early is crucial in preventing vision impairment. YOLO (You Only Look Once) is a deep neural network algorithm renowned for its effectiveness in object detection tasks. Its speed and efficiency make it an excellent choice for real-time applications. In the mentioned research paper, the authors utilized YOLOv8 to detect cataracts. They achieved an accuracy of 95% using a dataset comprising 1015 images. We have also compared YOLOv8 with cataract detection algorithms like inception V3, VGGNet, AlexNet, and ResNet50. Notably YOLOv8 surpassed both algorithms in terms of accuracy and speed. Key advantages of utilizing YOLOv8 for cataract detection are as follows: 1. High accuracy, YOLOv8 achieved an accuracy rate of 96.1% on the cataract dataset surpassing algorithms, like VGG19 and ResNet50. 2. Fast processing, YOLOv8 stands out as an efficient algorithm making it well suited for real-time applications. 3. Cost effectiveness, being free and open source YOLOv8 offers affordability in its utilization. Possible applications of cataract detection using the YOLOv8 algorithm are as follows: 1. screening a number of individuals. 2. telemedicine services. 3. assisting decision-making. In conclusion, the YOLOv8 algorithm shows promise as a method for automated cataract detection.