A Novel Metaheuristic Approach for Image Contrast Enhancement Based on Gray-Scale Mapping

Luque−Chang, Alberto; Aranguren, Itzel; Pérez‐Cisneros, Marco; Valdivia, Arturo

doi:10.1007/978-3-030-70542-8_24

Cited by 1 publication

(1 citation statement)

References 35 publications

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“…9 Meta-heuristic algorithms 42,43 are computational intelligence paradigms mainly used for solving different complex optimization problems. Owing to their computational efficiency as well as superior performance in resource-constrained environments, meta-heuristic algorithms have been extensively used across the domains, including feature selection, 44 neural architecture search, 45,46 task scheduling, 47 handwritten script classification, 48 image contrast enhancement, [49][50][51] data clustering, 52 multilevel image thresholding, 53,54 and solving class imbalance problem 55,56 among others. Mostly, these algorithms are inspired from: (1) theory of evolution, such as Genetic Algorithm (GA) 57 and Differential Evolution 58 ; (2) natural behavior of organisms, such as the Whale Optimization Algorithm (WOA), 59 Cuckoo Search (CS) 60 and Flower Pollination Algorithm 61 ; (3) swarm intelligence, such as the Particle Swarm Optimization (PSO) 62 and the Grey Wolf Optimizer (GWO) 63 ; and (4) physical or scientific phenomena, such as the Gravitational Search Algorithm (GSA), 64 and the Multiverse Optimizer, 65 to name a few.…”

Section: Related Workmentioning

confidence: 99%

Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

Chattopadhyay

Kundu

Singh

et al. 2021

Int J of Intelligent Sys

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Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X-ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X-ray images, a more robust method is therefore required, which can be achieved by using a computer-aided diagnosis (CAD) system. In this study, we develop a two-stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and timeefficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet-201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing-based local search algorithm. The optimized feature subset is utilized for the classification of "Pneumonia" and "Normal" X-ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the

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