Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines

Abstract

Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification. Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs. Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination. Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions.

Figure 1

Melanoma is staged in four segments, depending upon the spread of the disease. Stage 0 is also called melanoma in situ: the melanoma cells are only in the outer layer of the skin (the epidermis); Stages I and II mean there are melanoma cells in the layer directly under the epidermis or touching on the next layer down but there is no sign that it has spread to lymph nodes or other parts of the body [11].

Figure 2

Dermoscopic images of micro-melanoma lesions (based on [1]).

Figure 3

Flowchart of the proposed automated image analysis system.

Figure 4

Outcome of the preprocessing step: (a) dermoscopic input image and (b) image after black frame removal and hair inpainting.

Figure 5

Dermoscopic image segmentation using region-growing method: (a) dermoscopic input image, (b) image after enhancement, and (c) segmented skin mole.

Figure 6

ROC plot for the SVM classifier (AUC = 93.24%).