Segmentation and Classification of Skin Lesions for Disease Diagnosis

Abstract

In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fusion for the classification using the extracted features. The performance of the system is tested on our own dataset of 726 samples from 141 images consisting of 5 different classes of diseases. The results are very promising with 46.71% and 34% of F-measure using SVM and k-NN classifier respectively and with 61% of F-measure for fusion of SVM and k-NN.

10 pages, 6 figures, 2 Tables in Elsevier, Proceedia Computer Science, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015)

Cited by in corpus (4)

• Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma
• Semantic Segmentation of Skin Lesions using a Small Data Set
• Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification
• Comparative Analysis of Automatic Skin Lesion Segmentation with Two Different Implementations