A novel hybrid meta-heuristic contrast stretching technique for improved skin lesion segmentation

Shairyar Malik, S. M. Riazul Islam, Talha Akram, Syed Rameez Naqvi, Norah Saleh Alghamdi, George Baryannis

Research output: Contribution to journalArticlepeer-review

Abstract

The high precedence of epidemiological examination of skin lesions necessitated the well-performing efficient classification and segmentation models. In the past two decades, various algorithms, especially machine/deep learning-based methods, replicated the classical visual examination to accomplish the above-mentioned tasks. These automated streams of models demand evident lesions with less background and noise affecting the region of interest. However, even after the proposal of these advanced techniques, there are gaps in achieving the efficacy of matter. Recently, many preprocessors proposed to enhance the contrast of lesions, which further aided the skin lesion segmentation and classification tasks. Metaheuristics are the methods used to support the search space optimisation problems. We propose a novel Hybrid Metaheuristic Differential Evolution-Bat Algorithm (DE-BA), which estimates parameters used in the brightness preserving contrast stretching transformation function. For extensive experimentation we tested our proposed algorithm on various publicly available databases like ISIC 2016, 2017, 2018 and PH 2, and validated the proposed model with some state-of-the-art already existing segmentation models. The tabular and visual comparison of the results concluded that DE-BA as a preprocessor positively enhances the segmentation results.

Original languageEnglish
Article number106222
Number of pages11
JournalComputers in Biology and Medicine
Volume151
Issue numberPart A
Early online date4 Nov 2022
DOIs
Publication statusE-pub ahead of print - 4 Nov 2022

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