Article
AN AUTOMATED SKIN CANCER DETECTION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS
Skin cancer is one of the most common types of cancer worldwide, and early detection plays a crucial role in improving patient survival rates. Traditional diagnosis methods rely heavily on dermatologists and manual examination of skin lesions, which can be time-consuming and subjective. This project proposes a deep learning-based system for automatic skin cancer classification using dermoscopic images. Convolutional Neural Networks (CNNs) are used to analyze and classify skin lesion images into different categories such as benign and malignant. The system processes images through preprocessing, feature extraction, and classification stages to accurately identify cancerous lesions. By using deep learning techniques, the proposed system improves diagnostic accuracy, reduces human error, and assists dermatologists in early detection and treatment planning. The proposed system processes input images, performs image preprocessing, feature extraction, and classification using deep learning techniques. By learning patterns from a large dataset of skin lesion images, the model can identify suspicious lesions with high accuracy. This approach helps in early detection of skin cancer, assists dermatologists in diagnosis, and reduces the chances of misclassification. Overall, the system provides an efficient and automated solution for skin cancer classification, improving diagnostic accuracy and supporting healthcare professionals in providing timely treatment.
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