A Hybrid Preprocessor DE-ABC for Efficient Skin-Lesion Segmentation with Improved Contrast

Abstract

Rapid advancements and the escalating necessity of autonomous algorithms in medical imaging require efficient models to accomplish tasks such as segmentation and classification. However, there exists a significant dependency on the image quality of datasets when using these models. Appreciable improvements to enhance datasets for efficient image analysis have been noted in the past. In addition, deep learning and machine learning are vastly employed in this field. However, even after the advent of these advanced techniques, a significant space exists for new research. Recent research works indicate the vast applicability of preprocessing techniques in segmentation tasks. Contrast stretching is one of the preprocessing techniques used to enhance a region of interest. We propose a novel hybrid meta-heuristic preprocessor (DE-ABC), which optimises the decision variables used in the contrast-enhancement transformation function. We validated the efficiency of the preprocessor against some state-of-the-art segmentation algorithms. Publicly available skin-lesion datasets such as PH2, ISIC-2016, ISIC-2017, and ISIC-2018 were employed. We used Jaccard and the dice coefficient as performance matrices; at the maximum, the proposed model improved the dice coefficient from 93.56% to 94.09%. Cross-comparisons of segmentation results with the original datasets versus the contrast-stretched datasets validate that DE-ABC enhances the efficiency of segmentation algorithms.

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

“…The parameter set as the global best is estimated through the hybrid metaheuristic technique to modify the intensity channel contrast. The parameter boundaries are reused as in our previous work [ 39 , 40 ] and are . For testing purposes, we have utilised three publicly available skin cancer datasets.…”

“…Optimisation algorithms demand the selection of proper bounds for the parameter set. We have reutilised the fine-tuned bounds for our parameters as assessed in our previous work [ 40 ]. The lower bound vector contains the lower possible values of our parameter set; in contrast, vector comprises the upper bounds.…”

“…We have proposed a novel differential evolution–bat algorithm (DE-BA) using hybrid metaheuristic for the preprocessing of skin lesion images [ 39 ], and another hybrid technique, differential evolution–artificial bee colony (DE-ABC), is presented in [ 40 ]. The proposed algorithms approximate the parameter set using our proposed novel methodologies.…”

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

“…The first step for all proposed systems is to improve dermatoscopic images. The Dermatoscopy images include noise and artifacts due to the variety of acquisition devices, which negatively affect the subsequent stages of image processing and lead to unreliable results [ 27 ]. So, the main purpose of pre-processing is to remove noise and artifacts such as air bubbles, hair, skin lines, low contrast between lesion borders, and light reflections when the gel is applied to the skin at the time of image capture.…”

Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization

“…Still, the issues like under and over segmentation may occur while evaluating the performance using larger data. www.ijacsa.thesai.org Skin lesion segmentation using the metaheuristic approach was designed by [33] through a hybrid optimization strategy. In this, differential evolution and the artificial bee colony algorithms were combined together for performing the segmentation task.…”