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

• Published in: Computers in biology and medicine 2022-12, Vol.151 (Pt A), p.106222-106222, Article 106222
• Main Authors: Malik, Shairyar, Islam, S. M. Riazul, Akram, Tallha, Naqvi, Syed Rameez, Alghamdi, Norah Saleh, Baryannis, George
• Format: Article
• Language: English
• Subjects: Algorithms; Background noise; Bat algorithm; Biology; Classification; Computers; Deep learning; Dermoscopy - methods; Differential evolution; Epidemiology; Evolutionary algorithms; Evolutionary computation; Experimentation; Heuristic methods; Heuristics; Humans; Image Processing, Computer-Assisted - methods; Lesions; Literature reviews; Machine learning; Medicine; Melanoma - diagnosis; Naturvetenskapernas didaktik; Neural networks; Optimization; Parameter estimation; Science education; Segmentation; Skin cancer; Skin diseases; Skin Diseases - diagnostic imaging; Skin lesion segmentation; Skin lesions; Skin Neoplasms - diagnosis; Stretching
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106222

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 PH2, 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. •Implementation of novel Meta-heuristic based contrast stretching algorithm.•A hybrid model for contrast enhancement is named DE-BA.•Datasets accommodated were ISIC-2016, 2017, 2018 and PH2 for experimentation.•Preprocessor validated by segmenting images with three segmentation algorithms.•Enhance image segmentation outperformed original image segmentation results.