Intelligent Skin Cancer Detection Using Hybrid Deep Learning and Machine Learning Model

Skin cancer is one of the most frequently diagnosed cancers globally, and early detection is a critical factor in enhancing the survival rate of patients. Conventional methods for diagnosis involve a visual inspection of the skin by dermatologists, which is a time-consuming and subjective process. However, with the emergence of large skin image datasets, the need for intelligent systems to support early detection has become increasingly important. In this project, an intelligent skin cancer detection system is designed and developed using a hybrid approach that integrates both deep learning and machine learning algorithms. A pre-trained convolutional neural network model is employed to extract key features from skin images, which are then classified using a Support Vector Machine to detect benign or malignant skin lesions. Experimental analysis on a standard dataset reveals that the proposed method is capable of delivering consistent and accurate diagnostic results, thus emphasizing the potential of AI-based systems in supporting skin health awareness.