Skin Cancer Classification Towards Melanoma Detection With Deep Learning Techniques

Abstract

Classification of skin lesions plays a crucial role in diagnosing various, local and gene related, medical conditions in the field of dermoscopy. Estimation of these biomarkers are used to provide some insight, while detecting cancerous cells and classifying the lesion as either benign or malignant. This paper presents groundwork for detection of skin lesions withcancerous inclination by segmentation and subsequent application of Convolution Neural Network(CNN) on dermoscopy images. Images included in ISIC-2019 were used as dataset. Images with skin lesions were segmented based on individual channel intensity thresholding. The resultant images were fed into CNN for feature extraction. The extracted features were then used for classification by an Artificial Neural Network (ANN) classifier. Previously, several approaches have been used for subject diagnostic with varying degree of success. However, room is still available for exploring other techniques for improving proportion of successfully detected malignant lesions. As compared to a previous best of 97%, methodology presented in this paper yielded an accuracy of 98.32%