Revolutionizing Skin Cancer Detection: Leveraging Machine Learning and Deep Learning for Precise Lesion Identification

Skin Cancer is one of the most prevalent and deadly malignancies, particularly the melanoma. Early identification of the disease is essential for enhanced healthcare support. Current diagnostic techniques often depend on the visual evaluations by dermatologists, which are useful but are subjected to accessibility and human error limitations. In order to enhance the feature extraction and classification from the dermoscopic images, a hybrid model based Convolutional Neural Network (CNN) makes use of the associated benefits of VGG-16 and EfficientNet Models. The study proposes a efficient and hybrid deep learning model that incorporates the key features of VGG-16 and EfficientNet architectures for timely detection of the cancer. Upon the performance evaluation of the proposed model, it has been observed that the model achieves high accuracy of 96.3 per cent in distinguishing between benign and malignant lesions. The model has achieved high recall and precision values of 94.4 per cent and 95.7 per cent respectively, that is more as comparable to other diagnostic models and professional diagnosis in identifying cancer-indicative pattern. The main objective of AI-powered skin cancer detection system is to reduce the expenses involved in pathological testing, increase the accessibility and improve the early and efficient disease diagnosis.