A Novel Melanoma Diagnosing System Using Multi-directional Pattern Extraction-Based Distributed Deep Learning Model

Skin cancer is considered among the most threatening types of cancer. The main cause of skin cancer is unrepaired DNA breaks in the skin cells, which lead to inherited defects or irregularities on the skin. It is essential to detect skin cancer early on since it is more curable then and tends to progress gradually to other body regions also. The high fatality rate, increasing prevalence of cases, and high cost of healthcare expenses associated with skin cancer make early detection of behaviors essential. Because of how serious these issues may get, researchers have developed a number of preventive detection methods for skin cancer. This paper suggests a unique deep neural network classifier for skin cancer recognition and classification that is based on pattern extraction. By employing Multi-directional Texture Pattern extraction(MDTP) with geometrical details, features that are predictive of cancerous regions will be extracted from the images. These criteria would be picked using Batched Quantum Intensity Spot optimization to improve the segmentation process precision. The model would then be trained using Distributed Deep Learning (DDL), enabling extremely accurate detection of malignant areas in skin images, in order to speed up and increase the segmentation process accuracy. The results of the proposed MDTP-DDL method is validated and assessed by using the public datasets and several performance measures.