A healthcare model to predict skin cancer using deep extreme machine learning

Abstract

In the modern world, skin diseases are the leading cause of death in humans. Malignant skin growths most commonly develop on areas of the body that are exposed to sunlight, but they can appear anywhere on the body. In the early stages, most skin tumors are treatable. Early and rapid detection of skin diseases can save a patient's life. Innovation makes it possible to detect skin diseases at an early stage. A biopsy is a formal technique for detecting malignant growths on the skin [1]. After the skin cells are removed, the example is sent to different research centers for testing. The interaction is difficult and time-consuming, and we have developed a framework for spotting skin malignant growths with SVM for the early detection of skin cancers. Patients benefit more from this. Diagnoses are made using image processing strategies and support vector machines (SVMs). Various pre-handling procedures for clamor evacuation and picture improvement are performed on the coloscopy picture of skin disease. A thresholding procedure is then used to divide the picture. The GLCM method should be used to separate a few highlights in the image. The classifier uses these elements as inputs. The data is then classified using SVM. A harmful or harmless image is classified