Application of novel DIRF feature selection algorithm for automated brain disease detection

Yaman, Süleyman; Ünlü, Elif Işılay; Güler, Hasan; Şengür, Abdulkadir; Acharya, U. Rajendra

doi:10.1016/j.bspc.2023.105006

Cited by 3 publications

(1 citation statement)

References 55 publications

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“… + + [ 44 ] 2020 Herzog, L + − + + [ 45 ] 2019 Bizzo, B + ? + + [ 46 ] 2007 Uchiyama, Y + + + + Overall high risk of bias [ 47 ] 2023 Yaman, S − − ? + [ 48 ] 2022 Arnold, T − ?…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

Bojsen,

Elhakim,

Graumann

et al. 2024

Insights Imaging

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Objectives This systematic review and meta-analysis aimed to assess the stroke detection performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and additionally to identify reporting insufficiencies. Methods PRISMA guidelines were followed. MEDLINE, Embase, Cochrane Central, and IEEE Xplore were searched for studies utilising MRI and AI for stroke detection. The protocol was prospectively registered with PROSPERO (CRD42021289748). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were the primary outcomes. Only studies using MRI in adults were included. The intervention was AI for stroke detection with ischaemic and haemorrhagic stroke in separate categories. Any manual labelling was used as a comparator. A modified QUADAS-2 tool was used for bias assessment. The minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist was used to assess reporting insufficiencies. Meta-analyses were performed for sensitivity, specificity, and hierarchical summary ROC (HSROC) on low risk of bias studies. Results Thirty-three studies were eligible for inclusion. Fifteen studies had a low risk of bias. Low-risk studies were better for reporting MI-CLAIM items. Only one study examined a CE-approved AI algorithm. Forest plots revealed detection sensitivity and specificity of 93% and 93% with identical performance in the HSROC analysis and positive and negative likelihood ratios of 12.6 and 0.079. Conclusion Current AI technology can detect ischaemic stroke in MRI. There is a need for further validation of haemorrhagic detection. The clinical usability of AI stroke detection in MRI is yet to be investigated. Critical relevance statement This first meta-analysis concludes that AI, utilising diffusion-weighted MRI sequences, can accurately aid the detection of ischaemic brain lesions and its clinical utility is ready to be uncovered in clinical trials. Key Points There is a growing interest in AI solutions for detection aid. The performance is unknown for MRI stroke assessment. AI detection sensitivity and specificity were 93% and 93% for ischaemic lesions. There is limited evidence for the detection of patients with haemorrhagic lesions. AI can accurately detect patients with ischaemic stroke in MRI. Graphical Abstract

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“… + + [ 44 ] 2020 Herzog, L + − + + [ 45 ] 2019 Bizzo, B + ? + + [ 46 ] 2007 Uchiyama, Y + + + + Overall high risk of bias [ 47 ] 2023 Yaman, S − − ? + [ 48 ] 2022 Arnold, T − ?…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

Bojsen,

Elhakim,

Graumann

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

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A comparative study of bread wheat varieties identification on feature extraction, feature selection and machine learning algorithms

Kılıçarslan,

Kılıçarslan

2023

Eur Food Res Technol

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Skin cancer diagnosis using CNN features with Genetic Algorithm and Particle Swarm Optimization methods

Başaran,

Çelik

2024

Transactions of the Institute of Measurement and Control

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Skin cancer is one of the most common types of cancer in the world. If skin cancer is not treated early, it also affects the diseased area under the skin and this threatens the treatment of the disease. In recent years, many diseases have been rapidly detected with high accuracy with artificial intelligence methods, and the treatment process has accelerated. Convolutional neural networks, one of the artificial intelligence methods, provide very detailed information about images, and extremely successful results are obtained in classifying images. In this study, first the data set was trained with the EfficientNetB0 model, which is one of the convolutional neural networks models. Then, with the fully connected layer of this model, deep features of the images were obtained. These deep features were obtained by selecting Particle Swarm Optimization and Genetic Algorithm optimization, and different feature combinations were created. Each of these selected feature sets was classified by the support vector machines method, and the best performance results were tried to be obtained. As a result, the success of the proposed model has been proven by obtaining an accuracy rate of 89.17%.

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Application of novel DIRF feature selection algorithm for automated brain disease detection

Cited by 3 publications

References 55 publications

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

A comparative study of bread wheat varieties identification on feature extraction, feature selection and machine learning algorithms

Skin cancer diagnosis using CNN features with Genetic Algorithm and Particle Swarm Optimization methods

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