A Framework for Human Skin Disease Classification Using Convolutional Neural Network

Abstract

One of the most dangerous types of cancer is skin cancer, it becomes a significant health hazard when not treated and detected on time. Skin cancer may spread to other parts of the body and complicate treatment if it is not detected in its early stages. Mainly it is the result of abnormal skin cell growth, usually the cells are stimulated by the sun for a long time. The early detection of skin tumors is a basic but highly complicated and expensive process due to the complexity of the diagnostic methods implicated. The identification of skin cancer by the location and cells involved augments the necessity of a very precise classifier for a successful diagnosis. Where the use of CNN in the recognition and classification of skin cancer, especially in skin lesion classification has been proposed to solve this issue. The utilized diagnosing method includes the utilization of image processing algorithms and deep learning models to increase accuracy and efficiency. Methods like image augmentation are then used for adding more rows to the dataset are used to scale up the dataset. This way, the model understands the diverse cases encountered. In addition, transfer learning is useful for increasing the classification accuracy by using pre-trained models for improved performance. As one of deep learning's deep architectures, CNNs serves as a key player in the extraction of features and in the classification of skin problems like psoriasis. This technique has been impressively productive for it gets a hit rate of 75%, thus revealing future prospects in the medical field.