Image Segmentation Based Automated Skin Cancer Detection Technique

Abstract

Skin cancer is a prevalent and deadly disease that affects millions of people worldwide. Early detection and diagnosis of skin cancer can significantly improve the chances of successful treatment and recovery. This study proposes a skin cancer segmentation and detection system using image processing and deep learning techniques to automate the diagnosis process. The system is trained on a dataset of skin images and uses a deep learning algorithm to classify skin lesions as benign or malignant. The performance of the system is evaluated using various metrics, including accuracy, precision, recall, and F1 score. The results show that the proposed system achieves high accuracy in detecting and classifying skin lesions as benign or malignant. Additionally, the proposed system is compared with other state-of-the-art methods, and it is found that the proposed system outperforms them in terms of accuracy and speed. The study contributes to the advancement of deep learning and image-processing techniques for medical diagnosis and detection. The proposed system can have significant implications in improving the accuracy and speed of skin cancer diagnosis, thereby improving the chances of successful treatment and recovery.