SkinNet: An Improved Skin Cancer Classification System Using Convolutional Neural Network

Abstract

According to current research, skin cancer is now considered to be among the most potentially lethal types of cancer that may occur in humans. Early detection of skin cancer, especially malignant type, can be tremendously advantageous as it may increase the survival rate of patients. Computers can help in the medical field by assisting with diagnosis. A convolutional neural network and image processing technology-based automated system were utilized in this study to recognise skin cancer. The system receives images of skin lesions, which are examined to determine the presence of skin cancer. The most prominent result of our computer based investigation is that it provides accurate results comparable with human analysis. In this research, we used the power of CNNs to bear on skin cancer recognition. We built a robust CNN model named SkinNet from scratch and trained it on the popular HAM10000 dataset. We then increased its performance using common techniques, such as the data balancing technique namely SMOTE, to address the issue of class imbalanced of data. We obtained vey good results, and we believe that in near future, CNNs will be able to outperform traditional diagnosis and probably replace expert dermatologists. Our proposed CNN architecture is capable of providing 98.60% of recognition accuracy on the data that has never been seen before. In fact, it will be enough to get significant results that could be used to enhance the survival rate of humans.