An Intelligent System For Skin Cancer Detection Using Deep Learning Techniques

Skin cancer diagnosis poses significant challenges in the medical field due to its varying presentations and the expertise required for accurate classification. The complexity of skin lesions and the need for specialized expertise often hinder efficient diagnosis. The proposed work provides a reliable and effective method for classifying skin cancer using convolutional neural networks. The CNN model uses deep learning to automatically identify complex patterns and features. The model allows for the accurate and automated classification of skin lesions into benign and malignant categories. The proposed model is trained on a vast and diverse dataset that includes different types of skin cancer, specifically the "Skin Cancer MNIST HAM10000" dataset, which comprises 10,000 images of various skin lesions. Data augmentation approaches are utilized to enhance performance even more, allowing the model to generalize well over a range of skin textures, lighting situations, and lesion sizes. In addition to expediting the identification of skin cancer, the suggested CNN-based method demonstrates impressive accuracy, achieving an overall accuracy rate of approximately 98.10%. The automatic classification system is a valuable addition to a doctor's toolkit, as skin cancer cases increase. Early detection aids in the fight against this crippling sickness, which ultimately leads to progress.