Deep Learning Empowered HealthCare Sector A Framework for Skin Cancer Detection Using CNN

Abstract

One of the deadliest types of cancer, skin cancer, particularly melanoma, kills thousands of people every year if it is not discovered in its early stages. An efficient and reliable diagnosis is the key to enhancing patient survival rates. Traditional diagnostic methods, such as dermatologists' eye examinations, are often random and susceptible to errors. Medical image analysis has revolutionized itself with the advent of deep learning and artificial intelligence. Convolutional Neural Networks (CNNs), which is one of the deep learning models, have been notable in conducting image classification tasks, making them a great fit for skin lesion detection and classification. To classify dermoscopic images into melanoma and non-melanoma classes, this research presents a CNN-based model that is composed of three convolutional layers and two dense layers. Additionally, a thorough literature research and comparative analysis were carried out, emphasizing the advantages over current models in terms of efficiency, accuracy, and simplicity of architecture. The work highlights how CNNs have the potential to be dependable medical diagnostic tools that lessen reliance on manual assessments and allow for scalable screening systems, particularly in areas with restricted access to dermatologists. Early skin cancer detection can be significantly increased by incorporating such automated technologies into healthcare, which would eventually save lives by enabling quicker diagnosis and treatment planning.

Keywords

Skin Cancer, malignant, scalable screening system, melanoma classes, healthcare