Open Access

ARTICLE

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Amina Bibi¹, Muhamamd Attique Khan¹, Muhammad Younus Javed¹, Usman Tariq², Byeong-Gwon Kang³, Yunyoung Nam^3,*, Reham R. Mostafa⁴, Rasha H. Sakr⁵

1 Department of Computer Science, HITEC University, Taxila, Pakistan
2 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Khraj, Saudi Arabia
3 Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Korea
4 Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt
5 Computer Science Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt

* Corresponding Author: Yunyoung Nam. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2022, 71(2), 2477-2495. https://doi.org/10.32604/cmc.2022.018917

Received 26 March 2021; Accepted 12 May 2021; Issue published 07 December 2021

Abstract

Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation and classification. In the lesion segmentation task, contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination. Subsequently, the best channel is selected and the lesion map is computed, which is further converted into a binary form using a thresholding function. In the lesion classification task, two pre-trained CNN models were modified and trained using transfer learning. Deep features were extracted from both models and fused using canonical correlation analysis. During the fusion process, a few redundant features were also added, lowering classification accuracy. A new technique called maximum entropy score-based selection (MESbS) is proposed as a solution to this issue. The features selected through this approach are fed into a cubic support vector machine (C-SVM) for the final classification. Results: The experimental process was conducted on two datasets: ISIC 2017 and HAM10000. The ISIC 2017 dataset was used for the lesion segmentation task, whereas the HAM10000 dataset was used for the classification task. The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher than the existing techniques.

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Citations