Segmentation and classification of skin lesions for disease diagnosis

Sumira, R. and Suhil, Mahamad and Guru, D. S. (2015) Segmentation and classification of skin lesions for disease diagnosis. Procedia Computer Science, 45. pp. 76-85.

Full text not available from this repository. (Request a copy)


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.

Item Type: Article
Uncontrolled Keywords: Computer-Aided Diagnosis (CAD) and Lesion Area Segmentation and Region Growing and SVM and K-NN
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: Shrirekha N
Date Deposited: 20 Jul 2019 06:16
Last Modified: 20 Jul 2019 06:16

Actions (login required)

View Item View Item