"Detection and Classification of Skin Cancer from Dermoscopic Images"

Abstract

Because skin cancer is one of the most common types of cancer in the world, it is essential to detect it at an early stage and with precision in order to provide effective therapy. The purpose of this work is to investigate the concept of developing and evaluating a computational method for the identification and classification of skin cancer through the use of dermoscopic images. For the purpose of identifying and classifying skin lesions, we provide a novel framework that makes use of sophisticated image processing techniques and machine learning algorithms. For the purpose of enhancing the quality of dermoscopic images, our method incorporates preprocessing processes such as noise reduction and image enhancement. After that, we make use of convolutional neural networks, also known as CNNs, in order to extract pertinent information and categorise skin lesions into either benign or malignant categories. An evaluation of the suggested approach is carried out on a dataset that is accessible to the general public. The effectiveness of the method is demonstrated by a number of performance measures, which include accuracy, sensitivity, and specificity. In light of the findings, it appears that our model is capable of achieving a high level of classification accuracy, which surpasses the capabilities of conventional methods and demonstrates potential for real-world applications in dermatology. The purpose of this work is to aid physicians in making decisions on patient treatment that are more informed and make a contribution to the ongoing efforts that are being made to automate the diagnosis of skin cancer.