DeepNet: Automated Skin Disease Classification using DenseNet for Accurate Dermatological Diagnosis

The increasing burden of skin conditions calls for the need for effective and accurate diagnostic tools to enhance patient outcomes. This paper examines the application of DenseNet, known for its remarkable feature reuse as well as strong gradient flow, in automating the classification process of skin diseases including melanoma, squamous cell carcinoma, and benign lesions. The model achieved outstanding performance exceeding 97% accuracy by applying data augmentation techniques with precision and recall rates surpassing 97%. It is just those subtle distinctions among complex dermatological images that permit the system to differentiate between nearly coincident skin pathologies. To ensure accessibility and real-time diagnosis, the developed model was put into a friendly web application built with Streamlit, where a user can upload images of skin diseases for instant analysis. The system is designed to complement the diagnostic process generally in areas that are underserved or remote with limited access to dermatological expertise. Providing accurate and quick diagnoses contributes significantly to assisting healthcare professionals and improving the healthcare delivery system, thereby ultimately achieving more accurate, timely diagnoses and better patient care.