Brain tumor classification in MRI imagesusing genetic algorithm appended CNN

• Autorzy: Balamurugan Thiyagu , Gnanamanoharan E.

Identyfikatory

DOI

10.24423/cames.649

• Języki publikacji: EN

Abstrakty

EN

Brain tumors are fatal for majority of the patients, the different nature of the tumorcells requires the use of combined medical measures, and categorizing such tumors isa difficult task for radiologists. The diagnostic structures based on PCs have been offeredas an aid in diagnosing a brain tumor using magnetic resonance imaging (MRI). Generalfunctions are retrieved from the lowest layers of the neural network, and these lowestlayers are responsible for capturing low-level features and patterns in the raw input data,which can be particularly unique to the raw image. To validate this, the EfficientNetB3pre-trained model is utilized to classify three types of brain tumors: glioma, meningioma,and pituitary tumor. Initially, the characteristics of several EfficientNet modules are takenfrom the pre-trained EfficientNetB3 version to locate the brain tumor. Three types of braintumor datasets are used to assess each approach. Compared to the existing deep learningmodels, the concatenated functions of EfficientNetB3 and genetic algorithms give betteraccuracy. Tensor flow 2 and Nesterov-accelerated adaptive moment estimation (Nadam)are also employed to improve the model training process by making it quicker and better.The proposed technique using CNN attains an accuracy of 99.56%, a sensitivity of 98.9%,a specificity of 98.6%, an F-score of 98.9%, a precision of 98.9%, and a recall of 99.54%.

Słowa kluczowe

EN

deep learning; convolutional neural networks; EfficientNetB3; genetic algorithm; brain tumor classification

PL

głęboka nauka; splotowe sieci neuronowe; EfficientNetB3; algorytm genetyczny; klasyfikacja nowotworów mózgu

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2023
• Tom: Vol. 30, no. 3
• Strony: 305--321
• Opis fizyczny: Bibliogr. 27 poz., il., rys., tab., wykr.

Twórcy

autor

Balamurugan Thiyagu

• Department of Electronics and Communication Engineering, Government Collegeof Engineering, Dharmapuri, Tamilnadu, India

autor

Gnanamanoharan E.

• Department of Electronics and Communication Engineering, Annamalai University,Chidambaram, Tamilnadu

Bibliografia

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