An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102459 - 102459

Published: June 28, 2024

Brain tumors must be classified to determine their severity and appropriate therapy. Applying Artificial Intelligence medical imaging has enabled remarkable developments. The presented framework classifies patients with brain high accuracy efficiency using CNN, pre-trained models, the Manta Ray Foraging Optimization (MRFO) algorithm on X-ray MRI images. Additionally, CNN Transfer Learning (TL) hyperparameters will optimized through MRFO, resulting in improved performance of model. Two public datasets from Kaggle were used build models. first dataset consists two classes, while 2nd includes three contrast-enhanced T1-weighted classes. First, a patient should diagnosed as "Healthy" (or "Tumor"). When scan returns result "Healthy," no abnormalities brain. If reveals that tumor, an performed them. After that, type tumor (i.e., meningioma, pituitary, glioma) identified second proposed classifier. A comparative analysis models two-class showed VGG16's model outperformed other Besides, Xception achieved best results three-class dataset. manual review misclassifications was conducted reasons for correct evaluation suggested architecture yielded 99.96% X-rays 98.64% MRIs. deep learning most current

Language: Английский

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

Soft Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 15, 2025

Language: Английский

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Convolutional Neural Network with Coordinate Attention Module for the Detection of Skin Cancer

International journal of intelligent engineering and systems, Journal Year: 2024, Volume and Issue: 17(3), P. 341 - 351

Published: May 3, 2024

Skin cancer is one of the most common types globally, by increasing occurrence rates each year.It a predominant kind which rises from uncontrolled growth abnormal skin cells due to genetic mutations including various factors such as UV radiation, genetics and other factors.The death rate decreased when detected at early stages.Therefore, this paper proposed Convolutional Neural Network (CNN) with Coordinate Attention Module (CAM) for detection cancer.The data augmentation utilized in experiment pre-processing fed into Gray Level Co-occurrence Matrix (GLCM) based feature extraction technique.The Harris Hawk Optimization (HHO) selecting features that have faster convergence strong capability local optima.The selected are given input CNN CAM approach.This model estimated on ISIC-2019 ISIC-2020 datasets attains better results using accuracy, precision, recall, specificity, F1-score.The obtained result shows achieves accuracy 98.77% dataset 99.51% ensures accurate compared existing methods like Inception-ResNet Residual Deep (RDCNN).

Language: Английский

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Framework for segmentation, optimization, and recognition of multivariate brain tumors

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 32

Published: Nov. 1, 2024

Language: Английский

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