Skin Cancer Detection With Machine Learning

Abstract

a widespread problem today. Skin infections are rising at an ever-increasing rate. Additionally, skin conditions are difficult for human eyes to diagnose. Therefore, we suggested using a CNN (Convolutional Neural Network) system to identify and categorize skin diseases. The dataset we are utilizing is HAM10000 and some raw images. 2144 dermoscopic pictures of skin conditions, broken down into 7 classifications, are encompassing. Attributable to this, our CNN system will indeed be able to classify and pinpoint seven different types of skin diseases. In our system, we also do some picture preprocessing and image augmentation. In our system, we also do some picture preprocessing and image augmentation. ResNet50 is the pre-trained CNN models that we are operating. Skin cancer is an alarming disease for mankind. The necessity of early diagnosis of the skin cancer has been increased because of the rapid growth rate of Melanoma skin cancer, it’s high treatment costs, and death rate. This cancer cells are detected manually and it takes time to cure in most of the cases. This paper proposed an artificial skin cancer detection system using image processing and machine learning method. The features of the affected skin cells are extracted after the segmentation of the dermoscopic images using feature extraction technique. A deep learning based method convolutional neural network classifier is used for the stratification of the extracted features. An accuracy of 89.5% have been achieved after applying the publicly available data set Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel-based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 89%.