Detection of Skin Cancer Using VGG-16 Model

Skin cancer is a major public health melanoma, is one of the most aggressive and deadly forms of skin cancer. Early detection is crucial for improving survival rates, and advancements in machine learning, particularly Convolutional Neural Networks (CNNs), have shown promise in automating the diagnosis of skin lesions. This report focuses on the development and evaluation of a CNN-based model for predicting melanoma from dermoscopic images. The model leverages the deep learning capabilities of CNNs to automatically extract relevant features from input images, enabling accurate classification of benign and malignant lesions. The dataset used for training and testing the model comprises thousands of labeled skin images, which include various stages and types of melanoma and non-melanoma lesions. Through preprocessing steps, including normalization and augmentation, the model is trained to handle variations in image quality and environmental factors. The CNN model's architecture is designed to optimize accuracy, reduce over fitting, and enhance generalization. Evaluation metrics such as accuracy, precision, recall, and the F1-score are employed to assess the model's performance. The results demonstrate that the proposed CNN model achieves high classification accuracy, outperforming traditional image-processing techniques, and shows significant potential as a tool for aiding dermatologists in the early detection and diagnosis of melanoma. The study highlights the importance of AI in healthcare and the potential for future improvements in skin cancer diagnosis.