Skin Lesion Segmentation and Multiclass Classification using Deep Neural Networks with Transfer Learning Models

The skin, a vital protective barrier of the human body, plays a pivotal role in shielding from external elements.Identifying skin diseases early is crucial to prevent their progression into potentially life-threatening conditions like skin cancer.However, distinguishing these diseases at an early stage, when they bear a resemblance to one another, poses a significant challenge.Addressing this challenge necessitates the development of an innovative automated system adept at swiftly and accurately detecting various skin lesions depicted in biomedical images.In the field of healthcare, deep learning has shown promise, garnering considerable attention for its potential in managing a spectrum of disorders, including skin conditions.This work explores skin cancer classification methodologies aimed at preventing the progression of its consequences.It focuses on a process that categorizes skin cancer into seven distinct classes.The proposed approach involves two key steps: Segmentation followed by subsequent Classification.The segmentation phase leverages some models to delineate regions of interest, while the subsequent classification stage employs transfer learning models.These transfer learning models utilize the segmented mask images as input for precise classification.