Robust Skin Disease Classification Leveraging EfficientNet-B7

Skin diseases present a substantial challenge in healthcare, where accurate and prompt diagnosis is critical for effective treatment and patient management. Traditional diagnostic techniques, such as visual inspection by dermatologists, often involve subjectivity, extended evaluation time, and reliance on specialist availability. These constraints are particularly pronounced in regions with limited medical resources, where access to dermatology experts is scarce. Recent advancements in deep learning offer a promising avenue for automating skin disease diagnosis, potentially enhancing both accessibility and diagnostic consistency. This study investigates the use of EffectNet-B7, Modern Deep learning model intended for the classification of skin diseases. In this paper, we investigate the categorization of skin disease utilizing a most advanced deep learning model — EfficientNet-B7. We take the ISIC dataset to train the model, using transfer learning and fine-tuning strategies to optimize the feature extraction and enhance the classification accuracy. On the experimental side, EfficientNet-B7 has demonstrated a staggering 96% accuracy rate, making it an invaluable aide for dermatologists in their decision-making process in a clinical setting. In this work, results show that deep learning will enable the accurate and reliable performance of dermatological diagnostic processes, offering advanced levels of diagnostic implementation on a wider scale.