Prevention and Early Detection Strategies for Melanoma and Skin Cancer Using a Hybrid Deep Learning Model

Melanoma is a very dangerous kind of skin cancer, yet most skin cancers are treatable if detected early. Consequently, early detection is crucial. The increasing incidence of melanoma highlights the critical need for automated detection and preventive methods to facilitate speedy diagnosis. Due to the significant degree of resemblance across skin cancer photos, emerging computational approaches can increase diagnostic accuracy, while dermoscopy has long been the gold standard for visual diagnosis in dermatology. The Hybrid CNLSTM is introduced in this research as a novel architecture for improved melanoma detection. The proposed approach employs image processing techniques to detect affected skin regions based on visual traits. The HFE component merged feature vectors several feature extraction methods into one, with an emphasis on the HOG, and employs SLS for precise colour segmentation. The model was evaluated for unknown variables in melanoma classification after being trained to an impressive 96.59% accuracy. Based on the results, it’s clear that the Hybrid CNLSTM model is the way to go for better skin cancer detection. The importance of sophisticated deep learning frameworks in melanoma early detection and treatment planning is emphasized in this study.