An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics

Shairyar Malik; Tallha Akram; Muhammad Awais; Muhammad Attique Khan; Myriam Hadjouni; Hela Elmannai; Areej Alasiry; Mehrez Marzougui; Usman Tariq

doi:10.3390/diagnostics13071285

An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics

Diagnostics (Basel). 2023 Mar 28;13(7):1285. doi: 10.3390/diagnostics13071285.

Authors

Shairyar Malik¹, Tallha Akram¹, Muhammad Awais¹, Muhammad Attique Khan², Myriam Hadjouni³, Hela Elmannai⁴, Areej Alasiry⁵, Mehrez Marzougui⁵, Usman Tariq⁶

Affiliations

¹ Department of Electrical and Computer Engineering, Wah Campus, COMSATS University Islamabad, Wah Cantt 47040, Pakistan.
² Department of CS, HITEC University, Taxila 47080, Pakistan.
³ Department of Computer Sciences, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
⁴ Department of Information Technology, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
⁵ College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia.
⁶ Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.

Abstract

The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results. Many studies resolve the matter partly. However, there exists plenty of room for new research in this field. Recently, many algorithms have been presented to preprocess skin lesions, aiding the segmentation algorithms to generate efficient outcomes. Nature-inspired algorithms and metaheuristics help to estimate the optimal parameter set in the search space. This research article proposes a hybrid metaheuristic preprocessor, BA-ABC, to improve the quality of images by enhancing their contrast and preserving the brightness. The statistical transformation function, which helps to improve the contrast, is based on a parameter set estimated through the proposed hybrid metaheuristic model for every image in the dataset. For experimentation purposes, we have utilised three publicly available datasets, ISIC-2016, 2017 and 2018. The efficacy of the presented model is validated through some state-of-the-art segmentation algorithms. The visual outcomes of the boundary estimation algorithms and performance matrix validate that the proposed model performs well. The proposed model improves the dice coefficient to 94.6% in the results.

Keywords: artificial bee colony; bat algorithm; computer vision; deep learning; machine learning; skin lesion segmentation.

Grants and funding

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Small Groups Project under grant number (R.G.P.1/257/43). This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R193), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.