Cancer Detection Using Deep Learning

Abstract

Abstract: One of the most prevalent diseases is cancer. Cancer is a dangerous condition with a high death rate if it is not found and treated at an early stage. This study aims to build a precise and impartial model for detecting two types of cancers (Skin Cancer, and Oral Cancer) using Deep-Learning techniques. Machine learning and computer vision techniques, particularly Convolutional Neural Networks (CNNs), help to develop this system. Deep learning techniques can help us to detect and classify skin cancer from dermoscopy images, and oral cancer from histopathological images. Cancer will be more correctly identified with Convolutional Neural Network (CNN) technology. It increases the likelihood of curing cancer before it spreads. The advantage of using CNN architectures for cancer detection is that they can learn complex features from the images and classify them. This paper proposes a deep-learning-based approach for the diagnosis of skin cancer, and oral cancer based on predefined CNN architecture DenseNet. This model intends to improve detection and clinical decision-making. It helps healthcare to fight against cancer.