Revolutionizing Skin Disease Detection through Deep Transfer Learning and Squirrel Search Optimization
K. Muthulakshmi, S. Maruthuperumal, G. Rosline Nesa Kumari, Prabhakar Krishnan
Abstract
The timely and accurate detection of skin diseases is a critical aspect of modern healthcare, with a particular emphasis on addressing the complexities associated with varied dermatological conditions. Traditional diagnostic methods face formidable hurdles, necessitating the introduction of innovative solutions. This study unveils a groundbreaking methodology, ESSO (Efficient Net and Squirrel Search Optimization), tailored to augment skin disease detection with a focus on diverse dermatological conditions. The integration of Efficient Net architecture and the inventive Squirrel Search Optimization technique signifies a noteworthy breakthrough in dermatological diagnostics, promising heightened precision in identifying a spectrum of skin diseases. The intricate nature of skin disease diagnosis, marked by diverse and subtle manifestations, demands a specialized approach. The ESSO methodology is meticulously crafted to surmount these challenges, offering a robust solution for discerning complex skin conditions. By harnessing the synergies between Efficient Net and Squirrel Search Optimization, our model not only showcases efficiency in its diagnostic capabilities but also emphasizes its potential impact on early intervention across various skin diseases. Driven by an unwavering commitment to advancing healthcare outcomes, this research represents a critical stride towards the development of accurate and efficient tools for early skin disease detection. The achieved accuracy of 98.62% positions the ESSO model as a beacon of hope in the quest for reliable and precise solutions within the broader domain of skin disease detection. The subsequent sections will delve into the methodology, experimental findings, and the broader implications of ESSO in revolutionizing skin disease diagnostics.