Improved Segmentation algorithm using PSO and K-means for Basal Cell Carcinoma Classification from Skin Lesions
Komal Sharma1, Sanjay Madaan2

1Komal Sharma, Dept. of CSE, University Institute of Engineering, Chandigarh University, Gharuan, Mohali, India.

2Sanjay Madaan, Dept. of CSE, University Institute of Engineering, Chandigarh University, Gharuan, Mohali, India.

Manuscript received on 21 September 2019 | Revised Manuscript received on 30 September 2019 | Manuscript Published on 01 October 2019 | PP: 87-97 | Volume-8 Issue-9S4 July 2019 | Retrieval Number: I11130789S419/19©BEIESP | DOI: 10.35940/ijitee.I1113.0789S419

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

Abstract: Skin carcinoma has been sighted as one of the prevalent forms of carcinomas, specifically amongst Caucasian offspring and pale-skinned population. Basal Cell Carcinoma (BCC) is a malevolent skin carcinoma and its classification in earlier stage is a biggest issue. Whilst curable with early detection, only extremely skilled specialists are likely to recognize the disease accurately from skin lesions Dermoscopic images. Since expertise has limited contribution, an automatized system capable of classifying disease can be helpful in saving lives, reducing unnecessary biopsies, and extra costs. On the way to achieve this objective, we proposed a BCC classification model that unifies recent advances in deep learning with Artificial Neural Network (ANN) structure, creating hybrid algorithm of K-means segmentation with Particle Swarm Optimization (PSO) that are capable of segmenting accurate skin lesions region from dermoscopy images, as well as examining the detected region and neighboring tissues for BCC. The proposed system is evaluated using the largest publicly accessible standard skin lesions dataset of dermoscopic images, containing BCC and Non-BCC images. When the evaluation parameters of proposed work are contrasted with a couple of other top-of-the-line techniques, the proposed technique accomplishes superior performance of 97.9% with respect to area under the curve (AUC) in distinguishing BCC from benignant lesions only through the extricated Speeded Up Robust Features (SURF).

Keywords: Artificial Neural Network (ANN), Computer Assisted Dermoscopy, Pattern recognition, Particle Swarm Optimization (PSO), Skin Lesion, Skin Lesion Segmentation, Speeded up Robust Features (SURF) analysis on Medical Image
Scope of the Article: Classification