Ensemble-based deep learning architecture for medical image segmentation

Abstract

In recent years, skin cancer has overtaken all other types of cancer in frequency and is steadily growing. Skin cells will be harmed by severe and continuous sun burn. Sunburn is a risk factor for skin cell deterioration, yet there as seen in Figure 1.1, there is a decades-long lag time between sunburn and the development of skin lesions. Biomarkers are molecules that, when found or assessed, reveal details about a disease that goes beyond the usual clinical characteristics of a clinician.In the United States, two out of every ten persons suffer from skin diseases, the majority of which are connected to skin cancer (US). The two kinds of skin cancer are melanoma skin cancer (MSC) and non-melanoma skin cancer (NMSC). The four types of malignancies that make up NMSC are depicted in Figure 1.1. Examples of malignancies that affect the skin include Cutaneous Adnexal Carcinomas (CAC), Merkel Cell Carcinoma (MCC), Cutaneous Squamous Cell Carcinoma (CSCC), and Basal Cell Carcinoma (BCC). MSC is the most common malignancy after NMSC, causing 99% of all skin cancer fatalities but only constituting 1% of all skin ma lignancies. In the United States, more than 3.5 million NMSC cases are treated annually. Radiologists who use computer-aided diagnosis in clinical diagnosis can benefit from continuing research on automatic picture segmentation using medical imaging modalities. Its major objective is to replace medical image processing, which is impossible to improve, and to replace it with the infrastructure required for effective clinical diagnosis workflow. Medical image segmentation uses 2D or 3D medical pictures to manually, partially, or fully extract the area of interest (object). It is essential to analyse and evaluate the data set using computers due to its wide range of characteristics and size. It assists in defining the area of interest and provides anatomical details for clinical diagnosis and the benefit of radiologists.