Skin Disease Detection Using VGG16 and InceptionV3

Abstract

Accurate diagnosis and timely treatment of skin diseases present formidable challenges, posing potential health risks to individuals affected. This research paper delves into an extensive exploration of skin disease detection employing two renowned deep learning architectures: VGG16 and InceptionV3. A thorough and insightful comparison of their performance is provided. The study employs a diverse dataset comprising a spectrum of skin disease images, encompassing a variety of conditions, for both training and evaluation purposes.

The research methodology harnesses the power of transfer learning by leveraging pre-trained VGG16 and InceptionV3 models. This strategy obviates the necessity for manual feature engineering, enabling a proficient analysis of intricate inherent patterns within skin diseases. The dataset spans an array of skin disease cases, including melanoma, basal cell carcinoma, and squamous cell carcinoma, ensuring the model's adeptness at generalizing across diverse conditions.

To comprehensively assess each model's efficacy, an array of performance metrics including accuracy, precision, recall, and the F1 score are meticulously computed. The outcomes furnish invaluable insights into the merits and limitations of both architectural approaches, thereby facilitating an informed juxtaposition of their adeptness in accomplishing skin disease detection tasks.

The research findings underscore the potent capabilities of deep learning models, with specific emphasis on VGG16 and InceptionV3, in proficiently detecting and categorizing assorted skin diseases. The comparative analysis accentuates the divergent performance nuances between the two models, effectively shedding light on their respective strengths and weaknesses. These discernments hold potential utility for medical practitioners and researchers alike, guiding them in selecting the optimal model tailored to specific skin disease detection requisites.