2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 1 , PP: 118-128, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Shokhan M. Al-Barzinji 1 , Mohammed Q. Jawad 2 , Othman Mohammed Jasim 3 * , Zaid Sami Mohsen 4 , Omar Falah Al-Jumaili 5
Doi: https://doi.org/10.54216/JISIoT.170109
Diagnosis of brain tumors from MRI scans is a vital concern in medical imaging that contributes to the need for fast and accurate deep learning models. In this study, it is proposed a Hybrid CNN-ViT Feature Extraction framework that utilizes the local spatial feature extraction capability of Convolutional Neural Networks (CNNs) and long-range dependency capturing ability of Vision Transformers (ViTs). The method starts with a set of advanced preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and data augmentation based on generative adversarial networks (GAN) to help increase image quality and balance the dataset. First, trained by a CNN-based backbone is EfficientNet to obtain low- and mid-level spatial features, the hybrid model is proposed. These feature maps are further converted into patches and input to a Vision Transformer (ViT) encoder, where self-attention functions to refine global feature representations. The proposed method utilized concatenation and attention-based mechanism for feature fusion, which ensured the discriminative classification of features from both CNN and ViT. Finally, a fully connected layer with the softmax classifier predicts the presence of tumor and its kind. Extensive experiments have been conducted on benchmark brain MRI datasets, which show that the Hybrid CNN-ViT model significantly outperforms traditional CNN-based models and achieves higher accuracy, precision, recall, and F1-score. The study demonstrates the successful application of hybrid deep learning techniques for robust and generalizable brain tumor classification. The novelty of this research lies in integrating spatial information with context attention in enhancing AI-based medical diagnostics.
Brain Tumor Diagnosis , Convolutional Neural Networks , Vision Transformer , Feature Fusion , MRI , Deep&ensp , Learning , Medical Imaging
[1] A. Z. Shirazi et al., “The application of deep convolutional neural networks to brain cancer images: A survey,” J. Pers. Med., vol. 10, no. 4, pp. 1–27, 2020, doi: 10.3390/jpm10040224.
[2] M. Yousif, I. A. Ahmad, A. R. Hmeed, and A. A. Mukhlif, “Enhanced EEG Signal Classification Using Machine Learning and Optimization Algorithm,” Fusion Pract. Appl., vol. 17, no. 2, pp. 211–218, 2025, doi: 10.54216/FPA.170216.
[3] L. T. Al-Ameri, “Brain Endoscopy, a big neurosurgical revolution,” AL-Kindy Coll. Med. J., vol. 13, no. 2, pp. 1–5, 2018, doi: 10.47723/kcmj.v13i2.26.
[4] Z. Sobhaninia, S. Rezaei, N. Karimi, A. Emami, and S. Samavi, “Brain tumor segmentation by cascaded deep neural networks using multiple image scales,” 2020 28th Iran. Conf. Electr. Eng. ICEE 2020, pp. 11–14, 2020, doi: 10.1109/ICEE50131.2020.9260876.
[5] N. Ajlouni, A. Özyavaş, M. Takaoğlu, F. Takaoğlu, and F. Ajlouni, “Medical image diagnosis based on adaptive Hybrid Quantum CNN,” BMC Med. Imaging, vol. 23, no. 1, pp. 1–23, 2023, doi: 10.1186/s12880-023-01084-5.
[6] M. Arabahmadi, R. Farahbakhsh, and J. Rezazadeh, “Deep Learning for Smart Healthcare-A Survey on Brain Tumor Detection from Medical Imaging,” Sensors (Basel), vol. 22, no. 5, Mar. 2022, doi: 10.3390/s22051960.
[7] A. Mohammed Yousif, Noor M. Jassam, Ahmad Salim, Hussein Ali Bardan, Ahmed Farhan Mutlak, and A. F. A. D. Sallibi, “Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm,” no. January, 2025, doi: 10.54216/FPA.180211.
[8] S. S. Yadav and S. M. Jadhav, “Deep convolutional neural network based medical image classification for disease diagnosis,” J. Big Data, vol. 6, no. 1, p. 113, 2019, doi: 10.1186/s40537-019-0276-2.
[9] T. Felefly et al., “An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection,” J. Digit. Imaging, vol. 36, no. 6, pp. 2335–2346, 2023, doi: 10.1007/s10278-023-00886-x.
[10] J. Smith et al., “A Comprehensive Review on Deep Learning Techniques for Medical Image Analysis,” Int. J. Med. Inform., vol. 145, pp. 104-120, Mar. 2023, doi: 10.1016/j.ijmedinf.2023.104120.
[11] R. Kumar and P. Gupta, “Advanced Machine Learning Techniques for Medical Image Processing: A Review,” Comput. Biol. Med., vol. 143, p. 105309, Jan. 2022, doi: 10.1016/j.compbiomed.2022.105309.
[12] L. Chen and Y. Zhang, “Deep Learning for Medical Image Analysis: A Survey,” J. Biomed. Inform., vol. 118, p. 103811, Feb. 2021, doi: 10.1016/j.jbi.2021.103811.
[13] S. Solanki, U. P. Singh, S. S. Chouhan, and S. Jain, “Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview,” IEEE Access, vol. 11, no. February, pp. 12870–12886, 2023, doi: 10.1109/ACCESS.2023.3242666.
[14] S. Anantharajan, S. Gunasekaran, T. Subramanian, and V. R, “MRI brain tumor detection using deep learning and machine learning approaches,” Meas. Sensors, vol. 31, p. 101026, 2024, doi: https://doi.org/10.1016/j.measen.2024.101026.
[15] F. Jia, S. Li, and T. Zhang, “Detection of cervical cancer cells based on strong feature CNN-SVM network,” Neurocomputing, vol. 411, 2020, doi: 10.1016/j.neucom.2020.06.006.
[16] M. A. Mohammed, B. Al-Khateeb, M. Yousif, S. A. Mostafa, S. Kadry, and K. H. Abdulkareem, “Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model,” vol. 2022, 2022.
[17] A. W. Salehi et al., “A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope,” Sustain., vol. 15, no. 7, 2023, doi: 10.3390/su15075930.
[18] İ. PACAL, “Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images,” Iğdır Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 12, no. 4, pp. 1917–1927, 2022, doi: 10.21597/jist.1183679.
[19] M. Yousif and B. Al-Khateeb, “Quantum Deep Learning: A Review,” Iraqi J. Sci., vol. 65, no. 8, pp. 4588–4605, 2024, doi: 10.24996/ijs.2024.65.8.37.
[20] A. Manna, R. Kundu, D. Kaplun, A. Sinitca, and R. Sarkar, “A fuzzy rank-based ensemble of CNN models for classification of cervical cytology,” Sci. Rep., vol. 11, no. 1, pp. 1–18, 2021, doi: 10.1038/s41598-021-93783-8.
[21] Y. Shin, H. A. Qadir, L. Aabakken, J. Bergsland, and I. Balasingham, “Automatic colon polyp detection using region based deep CNN and post learning approaches,” IEEE Access, vol. 6, pp. 40950–40962, 2018, doi: 10.1109/ACCESS.2018.2856402.
[22] M. Yousif and B. Al-Khateeb, “Quantum Convolutional Neural Network for Image Classification,” Fusion Pract. Appl., vol. 15, no. 2, pp. 61–72, 2024, doi: 10.54216/FPA.150205.
[23] A. A. Asiri, A. Shaf, T. Ali, M. Aamir, M. Irfan, and S. Alqahtani, “Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability,” PeerJ Comput. Sci., vol. 10, pp. 1–28, 2024, doi: 10.7717/peerj-cs.1878.
[24] S. Ben Brahim, S. Dardouri, and R. Bouallegue, “Brain Tumor Detection Using a Deep CNN Model,” vol. 2024, 2024, doi: 10.1155/2024/7634426.
[25] M. Aamir et al., “Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network,” Diagnostics, vol. 14, no. 16, 2024, doi: 10.3390/diagnostics14161714.
[26] Z. Li and O. Dib, “Empowering Brain Tumor Diagnosis through Explainable Deep Learning,” Mach. Learn. Knowl. Extr., vol. 6, no. 4, pp. 2248–2281, 2024, doi: 10.3390/make6040111.
[27] M. S. Ullah, M. A. Khan, A. Masood, O. Mzoughi, O. Saidani, and N. Alturki, “Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm,” Front. Oncol., vol. 14, no. February, pp. 1–21, 2024, doi: 10.3389/fonc.2024.1335740.
[28] A. A. Akinyelu, F. Zaccagna, J. T. Grist, M. Castelli, and L. Rundo, “Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey,” J. Imaging, vol. 8, no. 8, pp. 1–40, 2022, doi: 10.3390/jimaging8080205.
