Skin Lesion Diagnosis Using Pretrained Models: A Study of Preprocessing and Feature Extraction

Image classification plays a significant role in the early detection of skin lesions, notably melanoma, an extremely prevalent and potentially harmful skin disease. However, the inherent data heterogeneity and large number of medical imagine databases make effective classification complicated. In response, this work provides an innovative deep learning-based technique to confronting these difficulties straight on. We combine complex neural networks with machine learning techniques, and our work is notable for its singular focus on feature extraction, which we do by combining 14 pre-trained models with machine learning algorithms. This combination guarantees that skin lesion photos are rigorously preprocessed and evaluated, yielding a significant increase in classification accuracy. Our study compares our methodology to state-of-the-art approaches in medical image analysis, highlighting our greater accuracy. This points to a possible paradigm change in melanoma detection and skin lesion classification. Our research assists both medical practitioners and patients by providing early identification and personalized therapy. It demonstrates our methodology’s uniqueness and evident superiority over previous approaches, furthering medical image analysis.