"SKIN CANCER CLASSIFICATOIN USING DEEP LEARNING"

Abstract

According to WHO, Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity. Generally, people face a lot of health-related problems and do not have the idea about the reason for those problems. Since, the healthcare industry produces a huge amount of data. Skin cancer is one of the top three perilous types of cancer caused by damaged DNA that can cause death. This damaged DNA begins cells to grow uncontrollably and nowadays it is getting increased speedily. However, analysis of these images is very challenging having some troublesome factors like light reflections from the skin surface, variations in color illumination, different shapes, and sizes of the lesions. In this project, we propose a convolutional neural network (CNN) model based on deep learning approach for the accurate classification of different types of skin cancerous cells. In preprocessing we firstly normalize the input images and extract features that help for accurate classification; and finally, data augmentation increases the number of images that improves the accuracy of classification rate. The model is evaluated on the HAM10000 dataset and ultimately, we obtained the highest 89% of training and 88% of testing accuracy respectively. The outcomes of our proposed CNN model define it as more reliable and robust when compared with existing transfer learning models.