SKIN CANCER DETECTION USING DEEP LEARNINIG

Skin cancer is one of the most common and life-threatening forms of cancer worldwide, with its incidence increasing due to factors such as ozone depletion and prolonged ultraviolet (UV) exposure. Early detection plays a critical role in improving survival rates; however, conventional diagnostic methods, such as dermatological examinations and biopsies, are time-consuming, expensive, and prone to subjectivity. Recent advancements in artificial intelligence, particularly deep learning, have shown significant potential in addressing these challenges by enabling automated, accurate, and efficient skin cancer detection.This research proposes a deep learning–based skin cancer detection system using Convolutional Neural Networks (CNNs) for the classification of dermoscopic images. The system leverages the HAM10000 dataset to classify seven skin cancer types, including melanoma and non-melanoma variants. Through preprocessing techniques such as resizing, normalization, and data augmentation, the proposed model ensures improved robustness and generalization. Furthermore, advanced architectures like ResNet50 are explored to enhance classification accuracy.The experimental results demonstrate that CNN-based approaches achieve high precision and reliability, significantly outperforming traditional methods. By integrating such models into mobile and cloud-based healthcare platforms, the system offers scalability, accessibility, and real-time diagnostic support, making it particularly useful in remote and resource-limited regions. The proposed work highlights the potential of deep learning to revolutionize dermatological screening, improve diagnostic accuracy, reduce reliance on invasive procedures, and ultimately contribute to better patient outcomes and global healthcare standards.