Skin Cancer Classification Using Deep Learning Techniques

Abstract

The most prevalent disease in the world is skin cancer. Dermatologists must have a high level of knowledge and precision when diagnosing skin cancer, hence a computer-aided skin cancer diagnosis model is suggested to offer a more impartial and trustworthy answer. Many studies have been conducted to aid in the detection of skin conditions like skin cancer and skin tumors. However, diagnosing the condition accurately is quite difficult due to factors like low contrast between lesions and skin, visual similarity between the diseased and unaffected areas, etc. The goal is to analyses skin images, identify skin cancers and analyses the images by adding filters to remove noise or other undesired elements, converting the images to grayscale to aid in processing, and getting useful information which is feature extraction. The application of deep learning techniques in the medical field is common for diagnosis. The model will be trained using Convolutional Neural Networks (CNN) techniques, which will then be utilized to diagnose the skin condition correctly. In order to decide, these algorithms employ feature values from photos as input. Dermatologists will find it much simpler to effectively identify and diagnose skin problems with this computer-aided technique.