A Logical Design of Robust Methodology to Detect and Classify Melanoma Disease using Hybrid Deep Learning Principles

Recent research has determined that the form of skin cancer known as melanoma is the deadliest. When detected early and treated surgically, patients have a far better chance of surviving. Melanomas have been classified using a number of different methodologies, but none of them have been able to generate an answer that is considered to be adequate. Because of this, there is a huge need for computerized techniques of diagnosis that may be applied to dermoscopy images. The provision of a completely automated system for improved melanoma classification is the fundamental objective of this research project. To this purpose, we have created a unique deep learning principle that we call the Hybrid Learning based Melanoma Classifier (HLMC). This innovative deep learning principle takes its cues from a more conventional kind of deep learning known as the Convolutional Neural Network (CNN). Our goal is to solve the challenge of melanoma classification. When it comes to classification, the use of segmented images allows us to achieve an accuracy of 97.34%, sensitivity of 94.31% and a specificity of 95.17%. A classification approach that is based on deep learning and image segmentation produces more accurate findings and increases diagnostic precision when these parameters are used. In their day-to-day clinical practice, medical professionals are going to consider the proposed technique to be an extremely helpful resource. For all the proposed melanoma detection scheme HLMC provides an efficient predictions to analyze the disease in proper way, in which the resulting section shows the proper proof to it through the respective outcomes.