Detection and Classification of Skin Cancer by Using a Parallel CNN Model

Abstract

Skin cancer is one of the most lethal of all cancers. When it is not diagnosed and handled at the beginning, it is supposed to extend to other areas of the body.It also occurs while the tissue is revealed to light from the sun, mainly due to the rapid development of skin cells. For early detection, a dependable automated system for skin lesion recognition is absolutely mandatory in order to minimize effort , time and human life.Both Image processing and deep learning are used in the technique for successful treatment of skin cancer.The paper suggest an automated techinic for skin cancer classification. The classification of 9 types of skin cancer has been done in this study.Also the performance and ability of deep convolutional neural networks (CNN) is observed..The dataset contains nine clinical types skin cancer ,such as -actinic keratosis,basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, vascular lesions. The objective is to establish a model that diagnoses skin cancer as well as classifies it into various classes through the Convolution Neural Network. The diagnosing methodology uses concept of image processing and deep learning.Through using different tactics of image augmentation, the number of images has also been enriched.Finally, the transfer learning approach is used to further improve the accuracy of the classification tasks.Approximately 0.76 weighted average precision, 0.78 weighted average recall , 0.76 weighted average f1-score, and 79.45 percent accuracy are shown by the proposed CNN method.