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Article

Convolutional Neural Network–Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification

1
Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, “Dunarea de Jos” University of Galati, 800146 Galati, Romania
2
The Modelling & Simulation Laboratory, “Dunarea de Jos” University of Galati, 47 Domneasca Str., 800008 Galati, Romania
3
Department of Automatic Control and Electrical Engineering, Faculty of Automation, Computers, Electrical, Engineering and Electronics, “Dunarea de Jos” University of Galati, 800146 Galati, Romania
*
Author to whom correspondence should be addressed.
J. Imaging 2024, 10(9), 235; https://doi.org/10.3390/jimaging10090235
Submission received: 22 August 2024 / Revised: 6 September 2024 / Accepted: 18 September 2024 / Published: 20 September 2024
(This article belongs to the Special Issue Learning and Optimization for Medical Imaging)

Abstract

This paper presents a hybrid study of convolutional neural networks (CNNs), machine learning (ML), and transfer learning (TL) in the context of brain magnetic resonance imaging (MRI). The anatomy of the brain is very complex; inside the skull, a brain tumour can form in any part. With MRI technology, cross-sectional images are generated, and radiologists can detect the abnormalities. When the size of the tumour is very small, it is undetectable to the human visual system, necessitating alternative analysis using AI tools. As is widely known, CNNs explore the structure of an image and provide features on the SoftMax fully connected (SFC) layer, and the classification of the items that belong to the input classes is established. Two comparison studies for the classification of meningioma tumours and healthy brains are presented in this paper: (i) classifying MRI images using an original CNN and two pre-trained CNNs, DenseNet169 and EfficientNetV2B0; (ii) determining which CNN and ML combination yields the most accurate classification when SoftMax is replaced with three ML models; in this context, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were proposed. In a binary classification of tumours and healthy brains, the EfficientNetB0-SVM combination shows an accuracy of 99.5% on the test dataset. A generalisation of the results was performed, and overfitting was prevented by using the bagging ensemble method.
Keywords: meningioma tumour; convolutional neural networks; machine learning; transfer learning meningioma tumour; convolutional neural networks; machine learning; transfer learning

Share and Cite

MDPI and ACS Style

Moldovanu, S.; Tăbăcaru, G.; Barbu, M. Convolutional Neural Network–Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification. J. Imaging 2024, 10, 235. https://doi.org/10.3390/jimaging10090235

AMA Style

Moldovanu S, Tăbăcaru G, Barbu M. Convolutional Neural Network–Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification. Journal of Imaging. 2024; 10(9):235. https://doi.org/10.3390/jimaging10090235

Chicago/Turabian Style

Moldovanu, Simona, Gigi Tăbăcaru, and Marian Barbu. 2024. "Convolutional Neural Network–Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification" Journal of Imaging 10, no. 9: 235. https://doi.org/10.3390/jimaging10090235

APA Style

Moldovanu, S., Tăbăcaru, G., & Barbu, M. (2024). Convolutional Neural Network–Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification. Journal of Imaging, 10(9), 235. https://doi.org/10.3390/jimaging10090235

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