Skin Cancer Detection Using Deep Learning—A Review

Abstract

Skin cancer is one the most dangerous types of cancer and is one of the primary causes of death worldwide. The number of deaths can be reduced if skin cancer is diagnosed early. Skin cancer is mostly diagnosed using visual inspection, which is less accurate. Deep-learning-based methods have been proposed to assist dermatologists in the early and accurate diagnosis of skin cancers. This survey reviewed the most recent research articles on skin cancer classification using deep learning methods. We also provided an overview of the most common deep-learning models and datasets used for skin cancer classification.

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

“…This can help physicians make better decisions, eliminate misdiagnosis and delay, enhance patient outcomes, increase efficiency, and lower costs. Deep learning algorithms have been proven to identify skin cancer with 90% accuracy, equivalent to or better than human doctors [ 25 ]. For years, convolutional neural networks (CNNs) have dominated medical image classification and diagnoses.…”

“…Some existing technologies may occasionally fail to generate exact, clear edges between various regions in the images during segmentation. Some authors have failed to address the preprocessing method, which might lead to image inaccuracy [ 25 ]. Variations : Variations in lesion shape and texture can lead to incorrect region segmentation, which then leads to the extraction of irrelevant features [ 26 ].…”

“…Some existing technologies may occasionally fail to generate exact, clear edges between various regions in the images during segmentation. Some authors have failed to address the preprocessing method, which might lead to image inaccuracy [ 25 ].…”

Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

“…While skin cancer is often treatable, early detection and precise diagnosis play a pivotal role in achieving favourable treatment results and enhancing patient survival rates [ 7 , 8 , 9 ]. Skin cancer detection has traditionally relied on a mix of visual examination and histopathological analysis, methods which are fraught with limitations in accuracy and scalability.…”

“…Despite these technological advancements, the difficulty in differentiating malignant from benign cases continues to hinder diagnostic accuracy. In recent years, deep neural networks, especially convolutional neural networks (CNNs), [ 15 ] have demonstrated significant potential in precisely detecting and categorizing skin cancer from medical images [ 7 , 16 , 17 ], such as those shown in Figure 1 . CNNs are a specialized class of neural networks ideally suited for tasks involving image classification, as they can autonomously acquire hierarchical representations of image features directly from raw pixel values [ 18 , 19 ].…”