Enhanced Skin Disease Detection and Classification System Using Deep Learning Technique

Abstract

Even in this era of advanced technology, physicians still encounter challenges in accurately identifying skin diseases. Severe skin conditions necessitate urgent hospitalization, yet the diagnostic process involves costly tests with delayed results, exacerbating the worsening of infections. In rural areas, patients often ignore initial symptoms due to limited medical services, allowing conditions to progress unchecked. This underscores the pressing need for highly accurate automated skin disease detection systems. In response to this demand, we developed a multiclass convolutional neural network model aimed at discerning between healthy and diseased skin. Employing Python, we constructed a program utilizing this convolutional neural network with pre-trained networks- AlexNet and VGG19 to address the issue effectively. The primary objective of our project was to categorize five different skin illnesses using input images. By providing our model with pictures of affected skin regions, we aimed to detect ailments like Eczema, Nail fungus, Melanoma, Bullous, and Vascular Tumor. Upon processing the input image, our model would accurately identify and output the specific illness affecting the skin. This streamlined approach significantly aids in timely and accurate diagnosis, crucial for early intervention and treatment of various skin conditions