Deep Learning-Driven Skin Disease Diagnosis: Advancing Precision and Patient-Centered Care

Abstract

Skin diseases are in the middle of the most prevalent conditions, arising from a myriad of factors including viralinfections, bacteria, allergies, and fungal pathogens. Appropriate detection of these conditions is essential for effective treatmentand management. Further, Deep learning methods are employed to enable early-stage detection, with a particular emphasis onthe pivotal role of feature extraction in the classification process. This research emphasizes the significance of a patient-centeredapproach, aiming to provide responsible and effective solutions for skin diagnoses. In pursuing more accurate and timely skincondition diagnoses, we turn to deep learning techniques, leveraging the HAM10000 dataset. Initially, we perform differentprepossessing techniques on selected datasets to handle class imbalance and a Convolutional Neural Network and fine-tune hyper-parameters such as with or without Dropout, CW, FL, and Using Global Average Pooling. Our technique excels in distinguishingdiverse skin, Gender, localization, and Cell types with reliable evaluation metrics such as precision, recall, FI Score, and specificity.Our technique not only subsidizes the healthcare field but also underscores the potential of advanced technologies in enhancingearly skin disease detection and medical decision-making.