Skin cancer recognition by using a neuro-fuzzy system

Abstract

Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%.

Keywords: fuzzy system; neural networks; neuro-fuzzy system; skin cancer.

Figure 1.

Studied skin cancer types.

Figure 2.

Types of skin cancer studied in this research.

Figure 3.

Processing image using ADOBE PHOTOSHOP.

Figure 4.

Processing image using VB.NET program.

Figure 5.

F1 irregularity index.

Figure 6.

F2 percent asymmetry.

Figure 7.

F3 red color variance.

Figure 8.

F9 spherical color coordinates L.

Figure 9.

F15 ratio red.

Figure 10.

Basic neural networks structure.

Figure 11.

Hierarchal neural network.

Figure 12.

Structure of fuzzy logic system.

Figure 13.

Hierarchical NN and Neuro-Fuzzy comparison.