Skin Disease Classification by Gabor and FCTH Filters with Ensemble Learning Models: A Machine Learning Approach in Dermatology

Experts must attain accurate diagnoses to provide proper treatments to millions of people so skin disease diagnosis stands as the world’s most general health issue. Visual examination performed by experts leads to dermatological diagnosis yet it shows subjectivity between observers and requires lengthy assessments conducted by specialists. The analysis of large datasets by machine learning systems enables expertise in intricate dermoscopic image visual patterns along with clinical photo and histology image patterns to support dermatologists who need early disease identification. Field advancement in modern skin disease classification happens through the combination of models and the implementation of contrastive learning approaches. The recent medical field development focuses on building dual-purpose learning systems that merge augmented data methodologies to analyse limited medical datasets with privacy protection mechanisms to obtain ethnicity-independent results. Medical practitioners began adopting clinical AI applications for dermatology after XAI frameworks built their trust in the system outputs. A learning model interpretation stands crucial for skin cancer measurement because it delivers doctor-friendly results that both remain dependable and easy to understand during lesion diagnosis. The paper explores contemporary dermatologic disease diagnosis using machine learning by integrating self-supervised learning with contrastive learning and generative AI and multimodal fusion techniques and real-time AI implementation. The research emphasizes both bias solution efforts and dataset representation improvement and lightweight AI development for extensive clinical access. The work serves as the foundation to improve skin disease diagnosis assisted by machine learning because it leads to developing next-generation solutions with precise scalable medical imaging technology for global healthcare.