Skin Cancer Detection: A Review Using Deep Learning Techniques

Abstract

Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

Keywords: deep learning; deep neural network (DNN); machine learning; melanoma; skin lesion; support vector machine (SVM).

Figure 1

The process of skin cancer detection. ANN = Artificial neural network; CNN = Convolutional neural network; KNN = Kohonen self-organizing neural network; GAN = Generative adversarial neural network.

Figure 2

Skin disease categories from International Skin Imaging Collaboration (ISIC) dataset [12].

Figure 3

Basic ANN structure [13].

Figure 4

Skin cancer detection using ANN [19].

Figure 5

Basic CNN Architecture [9].

Figure 6

Skin cancer diagnosis using CNN [37].

Figure 7

Basic KNN structure [58], BMU= Best matching unit.

Figure 8

GAN architecture [64].