Artemisinin optimization based on malaria therapy: Algorithm and applications to medical image segmentation

Displays, Год журнала: 2024, Номер 84, С. 102740 - 102740

Опубликована: Май 4, 2024

Язык: Английский

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Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm

Computers in Biology and Medicine, Год журнала: 2023, Номер 160, С. 106966 - 106966

Опубликована: Апрель 24, 2023

Язык: Английский

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Attention Deep Feature Extraction from Brain MRIs in Explainable Mode: DGXAINet

Diagnostics, Год журнала: 2023, Номер 13(5), С. 859 - 859

Опубликована: Фев. 23, 2023

Artificial intelligence models do not provide information about exactly how the predictions are reached. This lack of transparency is a major drawback. Particularly in medical applications, interest explainable artificial (XAI), which helps to develop methods visualizing, explaining, and analyzing deep learning models, has increased recently. With intelligence, it possible understand whether solutions offered by techniques safe. paper aims diagnose fatal disease such as brain tumor faster more accurately using XAI methods. In this study, we preferred datasets that widely used literature, four-class kaggle dataset (Dataset I) three-class figshare II). To extract features, pre-trained model chosen. DenseNet201 feature extractor case. The proposed automated detection includes five stages. First, training MR images with DenseNet201, area was segmented GradCAM. features were extracted from trained exemplar method. Extracted selected iterative neighborhood component (INCA) selector. Finally, classified support vector machine (SVM) 10-fold cross-validation. An accuracy 98.65% 99.97%, obtained for Datasets I II, respectively. higher performance than state-of-the-art can be aid radiologists their diagnosis.

Язык: Английский

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Enhancement of brain tumor classification from MRI images using multi-path convolutional neural network with SVM classifier

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 93, С. 106117 - 106117

Опубликована: Фев. 24, 2024

Язык: Английский

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Integration of CNN Models and Machine Learning Methods in Credit Score Classification: 2D Image Transformation and Feature Extraction

Computational Economics, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Язык: Английский

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Feature Selection for High Dimensional Datasets Based on Quantum-Based Dwarf Mongoose Optimization

Mathematics, Год журнала: 2022, Номер 10(23), С. 4565 - 4565

Опубликована: Дек. 2, 2022

Feature selection (FS) methods play essential roles in different machine learning applications. Several FS have been developed; however, those that depend on metaheuristic (MH) algorithms showed impressive performance various domains. Thus, this paper, based the recent advances MH algorithms, we introduce a new technique to modify of Dwarf Mongoose Optimization (DMO) Algorithm using quantum-based optimization (QBO). The main idea is utilize QBO as local search traditional DMO avoid its limitations. So, developed method, named DMOAQ, benefits from advantages and QBO. It tested with well-known benchmark high-dimensional datasets, comprehensive comparisons several methods, including original DMO. evaluation outcomes verify DMOAQ has significantly enhanced capability outperformed other compared experiments.

Язык: Английский

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Proposing a new approach based on convolutional neural networks and random forest for the diagnosis of Parkinson's disease from speech signals

Applied Acoustics, Год журнала: 2023, Номер 211, С. 109476 - 109476

Опубликована: Июнь 15, 2023

Язык: Английский

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Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

Applied Intelligence, Год журнала: 2023, Номер 53(15), С. 18630 - 18652

Опубликована: Фев. 6, 2023

Язык: Английский

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Optimizing the topology of convolutional neural network (CNN) and artificial neural network (ANN) for brain tumor diagnosis (BTD) through MRIs

Heliyon, Год журнала: 2024, Номер 10(16), С. e35083 - e35083

Опубликована: Июль 23, 2024

The use of MRI analysis for BTD and tumor type detection has considerable importance within the domain machine vision. Numerous methodologies have been proposed to address this issue, significant progress achieved in via deep learning (DL) approaches. While majority offered approaches using artificial neural networks (ANNs) (DNNs) demonstrate satisfactory performance Bayesian Tree Descent (BTD), none these research studies can ensure optimality employed model structure. Put simply, there is room improvement efficiency models BTD. This introduces a novel approach optimizing configuration Convolutional Neural Networks (CNNs) Artificial issue. suggested employs (CNN) purpose segmenting brain MRIs. model's configurable hyper-parameters are tuned genetic algorithm (GA). Multi-Linear Principal Component Analysis (MPCA) used decrease dimensionality segmented features pictures after they segmented. Ultimately, segmentation procedure executed an Network (ANN). In network (ANN), (GA) sets ideal number neurons hidden layer appropriate weight vector. effectiveness was assessed by utilizing BRATS2014 BTD20 databases. results indicate that method classify samples from two databases with average accuracy 98.6 % 99.1 %, respectively, which represents at least 1.1 over preceding methods.

Язык: Английский

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Machine Learning-Based Feature Extraction and Selection

Applied Sciences, Год журнала: 2024, Номер 14(15), С. 6567 - 6567

Опубликована: Июль 27, 2024

Over the last decade, technological advances have brought breakthroughs in landscape of data management, transmission, processing, and storage [...]

Язык: Английский

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