2024
DOI: 10.3390/math12050633
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Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Baidaa Mutasher Rashed,
Nirvana Popescu

Abstract: Predicting diseases in the early stages is extremely important. By taking advantage of advances in deep learning and fuzzy logic techniques, a new model is proposed in this paper for disease evaluation depending on the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) for classification, and the pre-trained DenseNet-201 model for feature extraction, in addition to the whale optimization algorithm (WOA) for feature selection. Two medical databases (chest X-ray and MRI brain tumor) for … Show more

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Cited by 5 publications
(1 citation statement)
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“…CNNs can learn more complicated features by stacking many convolutional layers and pooling layers, allowing them to successfully differentiate between distinct classes or categories of input [6]. CNNs have been used to analyze a variety of medical imaging modalities, including X-rays, Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, ultrasound images, and histopathological slides [7] [8]. These methods have been used for a variety of applications, including disease categorization, lesion detection, organ segmentation, and treatment planning [9].…”
Section: Introductionmentioning
confidence: 99%
“…CNNs can learn more complicated features by stacking many convolutional layers and pooling layers, allowing them to successfully differentiate between distinct classes or categories of input [6]. CNNs have been used to analyze a variety of medical imaging modalities, including X-rays, Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, ultrasound images, and histopathological slides [7] [8]. These methods have been used for a variety of applications, including disease categorization, lesion detection, organ segmentation, and treatment planning [9].…”
Section: Introductionmentioning
confidence: 99%