Ant Colony Optimization-Based Streaming Feature Selection: An Application to the Medical Image Diagnosis

Fahad, Labiba Gillani; Tahir, Syed Fahad; Shahzad, Waseem; Hassan, Mehdi; Alquhayz, Hani; Hassan, Rabia

doi:10.1155/2020/1064934

Cited by 14 publications

(5 citation statements)

References 31 publications

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“…ACO is a stochastic metaheuristic for solving complex optimization problems that are continuous. It is inspired by the foraging strategy used by real biological ants when they are trying to discover a simple path between their colony and a source of food [ 28 ]. The ants interact informally while hunting using pheromones to indicate their pathways and attract additional ants.…”

Section: Methodsmentioning

confidence: 99%

An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders

Alghawli

Taloba

2022

Computational Intelligence and Neuroscience

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Bipolar disorder is marked by mood swings that alternate between mania and depression. The stages of bipolar disorder (BD), as one of the most common mental conditions, are often misdiagnosed as major depressive disorder (MDD), resulting in ineffective treatment and a poor prognosis. As a result, distinguishing MDD from BD at an earlier phase of the disease may aid in more efficient and targeted treatments. In this research, an enhanced ACO (IACO) technique biologically inspired by and following the required ant colony optimization (ACO) was utilized to minimize the number of features by deleting unrelated or redundant feature data. To distinguish MDD and BD individuals, the selected features were loaded into a support vector machine (SVM), a sophisticated mathematical technique for classification process, regression, functional estimates, and modeling operations. In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.

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Section: Methodsmentioning

confidence: 99%

An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders

Alghawli

Taloba

2022

Computational Intelligence and Neuroscience

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“…Preprocessing and detection of lesion edges are very crucial steps in the automated detection of skin cancer. Various optimization algorithms such as artificial the bee colony algorithm [78], ant colony optimization [79], social spider optimization [80], and particle swarm optimization [81] can be explored to increase the performance of automated skin cancer diagnostic systems.…”

Section: Use Of Various Optimization Techniquesmentioning

confidence: 99%

Skin Cancer Detection: A Review Using Deep Learning Techniques

Dildar

Akram

Irfan

et al. 2021

IJERPH

320

109

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Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

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“…In 1992, J.H. Holland designed one of the metaheuristic algorithms that are most well-known today [28]. Chromosome representation, fitness selection, and physiologically inspired operators are a few of its elements.…”

Section: A Genetic Algorithmmentioning

confidence: 99%

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

Mandal,

Sarma,

Dehuri

2023

IJRITCC

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A self-learning disease detection model will be useful for identifying skin infections in suspected individuals using skin images of infected patients. To detect skin diseases, some AI-based bioinspired models employ skin images. Skin infection is a common problem that is currently faced due to various reasons, such as food, water, environmental factors, and many others. Skin infections such as psoriasis, skin cancer, monkeypox, and tomato flu, among others, have a lower death rate but a significant impact on quality of life. Neural Networks (NNs) and Swarm intelligence (SI) based approaches are employed for skin disease diagnosis and classification through image processing. In this paper, the convolutional neural networks-based Cuckoo search algorithm (CNN-CS) is trained using the well-known multi-objective optimization technique cuckoo search. The performance of the suggested CNN-CS model is evaluated by comparing it with three commonly used metaheuristic-based classifiers: CNN-GA, CNN-BAT, and CNN-PSO. This comparison was based on various measures, including accuracy, precision, recall, and F1-score. These measures are calculated using the confusion matrices from the testing phase. The results of the experiments revealed that the proposed model has outperformed the others, achieving an accuracy of 97.72%.

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Ant Colony Optimization-Based Streaming Feature Selection: An Application to the Medical Image Diagnosis

Cited by 14 publications

References 31 publications

An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders

An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders

Skin Cancer Detection: A Review Using Deep Learning Techniques

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

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