Breakthrough in Brain Tumor Diagnosis: A Cutting-Edge Hybrid Depthwise-Direct Acyclic Graph Network for MRI Image Classification

Authors

  • Felix Joseph X Professor, Department of Electrical and Electronics Engineering, Loyola Institute of Technology and Science, Thovalai, Tamilnadu, India.
  • Maithili Vijayakumar Department of Artificial Intelligence, R. M. K. College of Engineering and Technology, Tiruvallur, Chennai, India.
  • Sujatha Therese P Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamilnadu, India.
  • Josephin Shermila P Associate Professor, Department of Artificial Intelligence and Data Science, R. M. K. College of Engineering and Technology, Tiruvallur, Chennai, India.
  • Eugine Prince M Assistant Professor, Department of Physics, S.T.Hindu College, Nagercoil, Tamilnadu, India.
  • Maris Murugan T Associate Professor, Department of Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Erode, Tamilnadu, India.

DOI:

https://doi.org/10.37385/jaets.v6i1.5938

Keywords:

Brain tumor, Detection, Depthwise-Direct Acyclic Graph Network, Deep learning, Computer-Aided Diagnosis

Abstract

Both adults and children are at risk of dying from brain tumors. On the other hand, prompt and precise detection can save lives. Early detection is necessary for a proper diagnosis of a brain tumor, and magnetic resonance imaging (MRI) is often used in this context. To assist in the early diagnosis of sickness, neuro-oncologists have used Computer-Aided Diagnosis (CAD) in a number of ways. In this study, proposed a hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET)-based deep learning was developed to distinguish between cancers and non-tumors. Three processes make up this method: pre-processing, classification, and feature extraction. Pre-processing methods used in this study included contrast enhancement and image shrinking. The MRI picture is processed to get the wavelet and texture properties and used to build a classifier. Using MRI scans, the proposed hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model classifies two types of brain tumors: tumor and non-tumor. Performance criteria such as accuracy (ACC), specificity (SP), and sensitivity (SE) are used to assess the suggested hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model. Based on 850 images, the studies yielded a 99.54% categorization accuracy demonstrate that the suggested hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model beats the most advanced methods.

Downloads

Download data is not yet available.

References

Abdusalomov, A. B., Mukhiddinov, M., &Whangbo, T. K. (2023). Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers, 15(16), 4172. https://doi.org/10.3390/cancers15164172

Anaraki, A. K., Ayati, M., & Kazemi, F. (2019). Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. biocybernetics and biomedical engineering, 39(1), 63-74. https://doi.org/10.1016/j.bbe.2018.10.004

Amin, J., Sharif, M., Gul, N., Raza, M., Anjum, M. A., Nisar, M. W., & Bukhari, S. A. C. (2020). Brain tumor detection by using stacked autoencoders in deep learning. Journal of medical systems, 44, 1-12. https://doi.org/10.1007/s10916-019-1483-2

Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2020). A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognition Letters, 139, 118-127. https://doi.org/10.1016/j.patrec.2017.10.036

Arbane, M., Benlamri, R., Brik, Y., &Djerioui, M. (2021, February). Transfer learning for automatic brain tumor classification using MRI images. In 2020 2nd international workshop on human-centric smart environments for health and well-being (IHSH) (pp. 210-214). IEEE. https://doi.org/10.1109/IHSH51661.2021.9378739

Asiri, A. A., Soomro, T. A., Shah, A. A., Pogrebna, G., Irfan, M., & Alqahtani, S. (2024). Optimized Brain Tumor Detection: A Dual-Module Approach for MRI Image Enhancement and Tumor Classification. IEEE Access, 12, 42868-42887. https://doi.org/10.1109/ACCESS.2024.3379136

Brindha, P. G., Kavinraj, M., Manivasakam, P., & Prasanth, P. (2021, February). Brain tumor detection from MRI images using deep learning techniques. In IOP conference series: materials science and engineering (Vol. 1055, No. 1, p. 012115). IOP Publishing. https://doi.org/10.1088/1757-899X/1055/1/012115

Choudhury, C. L., Mahanty, C., Kumar, R., & Mishra, B. K. (2020, March). Brain tumor detection and classification using convolutional neural network and deep neural network. In 2020 international conference on computer science, engineering and applications (ICCSEA) (pp. 1-4). IEEE. https://doi.org/10.1109/ICCSEA49143.2020.9132874

Dipu, N. M., Shohan, S. A., & Salam, K. M. A. (2021, June). Deep learning based brain tumor detection and classification. In 2021 International conference on intelligent technologies (CONIT) (pp. 1-6). IEEE. https://doi.org/10.1109/CONIT51480.2021.9498384

Ghassemi, N., Shoeibi, A., & Rouhani, M. (2020). Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomedical Signal Processing and Control, 57, 101678. https://doi.org/10.1016/j.bspc.2019.101678

Gull, S., & Akbar, S. (2021). Artificial intelligence in brain tumor detection through MRI scans: advancements and challenges. Artificial Intelligence and Internet of Things, 241-276.

Gumaei, A., Hassan, M. M., Hassan, M. R., Alelaiwi, A., & Fortino, G. (2019). A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access, 7, 36266-36273. https://doi.org/10.1109/ACCESS.2019.2904145

Hemanth, G., Janardhan, M., & Sujihelen, L. (2019, April). Design and implementing brain tumor detection using machine learning approach. In 2019 3rd international conference on trends in electronics and informatics (ICOEI) (pp. 1289-1294). IEEE. https://doi.org/10.1109/ICOEI.2019.8862553

Jabbar, A., Naseem, S., Mahmood, T., Saba, T., Alamri, F. S., & Rehman, A. (2023). Brain tumor detection and multi-grade segmentation through hybrid caps-VGGNet model. IEEE Access, 11, 72518-72536. https://doi.org/10.1109/ACCESS.2023.3289224

Jia, Z., & Chen, D. (2020). Brain tumor identification and classification of MRI images using deep learning techniques. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3016319

Khan, M. A., Khan, A., Alhaisoni, M., Alqahtani, A., Alsubai, S., Alharbi, M., ... &Damaševi?ius, R. (2023). Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm. International Journal of Imaging Systems and Technology, 33(2), 572-587. https://doi.org/10.1002/ima.22831

Khan, M. S. I., Rahman, A., Debnath, T., Karim, M. R., Nasir, M. K., Band, S. S., ... &Dehzangi, I. (2022). Accurate brain tumor detection using deep convolutional neural network. Computational and Structural Biotechnology Journal, 20, 4733-4745. https://doi.org/10.1016/j.csbj.2022.08.039

Majib, M. S., Rahman, M. M., Sazzad, T. S., Khan, N. I., & Dey, S. K. (2021). Vgg-scnet: A vgg net-based deep learning framework for brain tumor detection on mri images. IEEE Access, 9, 116942-116952. https://doi.org/10.1109/ACCESS.2021.3105874

Maqsood, S., Damaševi?ius, R., &Maskeli?nas, R. (2022). Multi-modal brain tumor detection using deep neural network and multiclass SVM. Medicina, 58(8), 1090. https://doi.org/10.3390/medicina58081090

Methil, A. S. (2021, March). Brain tumor detection using deep learning and image processing. In 2021 international conference on artificial intelligence and smart systems (ICAIS) (pp. 100-108). IEEE. https://doi.org/10.1109/ICAIS50930.2021.9395823

Modiya, P., & Vahora, S. (2022). Brain tumor detection using transfer learning with dimensionality reduction method. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 201-206.

Muis, A., Sunardi, S., & Yudhana, A. (2024). Cnn-based approach for enhancing brain tumor image classification accuracy. International Journal of Engineering, 37(5), 984-996. https://doi.org/10.5829/ije.2024.37.05b.15

Nassar, S. E., Yasser, I., Amer, H. M., & Mohamed, M. A. (2024). A robust MRI-based brain tumor classification via a hybrid deep learning technique. The Journal of Supercomputing, 80(2), 2403-2427. https://doi.org/10.1007/s11227-023-05549-w

Rahman, T., & Islam, M. S. (2023). MRI brain tumor detection and classification using parallel deep convolutional neural networks. Measurement: Sensors, 26, 100694. https://doi.org/10.1016/j.measen.2023.100694

Rajinikanth, V., Joseph Raj, A. N., Thanaraj, K. P., & Naik, G. R. (2020). A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection. Applied Sciences, 10(10), 3429. https://doi.org/10.3390/app10103429

Ramanagiri, A., Mukunthan, M., & Balamurugan, G. (2024, April). Enhanced Brain Tumor Detection Using Resnet-50. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1708-1711). IEEE. https://doi.org/10.1109/ICCSP60870.2024.10543742

Rizwan, M., Shabbir, A., Javed, A. R., Shabbir, M., Baker, T., & Obe, D. A. J. (2022). Brain tumor and glioma grade classification using Gaussian convolutional neural network. IEEE Access, 10, 29731-29740. https://doi.org/10.1109/ACCESS.2022.3153108

Sadad, T., Rehman, A., Munir, A., Saba, T., Tariq, U., Ayesha, N., & Abbasi, R. (2021). Brain tumor detection and multi?classification using advanced deep learning techniques. Microscopy research and technique, 84(6), 1296-1308. https://doi.org/10.1002/jemt.23688

Saxena, P., Maheshwari, A., & Maheshwari, S. (2020). Predictive modeling of brain tumor: a deep learning approach. In Innovations in Computational Intelligence and Computer Vision: Proceedings of ICICV 2020 (pp. 275-285). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-6067-5_30

Shah, H. A., Saeed, F., Yun, S., Park, J. H., Paul, A., & Kang, J. M. (2022). A robust approach for brain tumor detection in magnetic resonance images using finetuned efficientnet. Ieee Access, 10, 65426-65438. https://doi.org/10.1109/ACCESS.2022.3184113

Suryawanshi, S., & Patil, S. B. (2024). Efficient brain tumor classification with a hybrid CNN-SVM approach in MRI. Journal of Advances in Information Technology, 15(3). https://doi.org/10.12720/jait.15.3.340-354

Solanki, S., Singh, U. P., Chouhan, S. S., & Jain, S. (2023). Brain tumor detection and classification using intelligence techniques: an overview. IEEE Access, 11, 12870-12886. https://doi.org/10.1109/ACCESS.2023.3242666

Tazeen, T., Sarvagya, M., & Sarvagya, M. (2021). Brain tumor segmentation and classification using multiple feature extraction and convolutional neural networks. International Journal of Engineering and Advanced Technology, 10(6), 23-27. 0621 https://doi.org/10.35940/ijeat.F2948.0810621

Tiwari, P., Pant, B., Elarabawy, M. M., Abd-Elnaby, M., Mohd, N., Dhiman, G., & Sharma, S. (2022). Cnn based multiclass brain tumor detection using medical imaging. Computational Intelligence and Neuroscience, 2022(1), 1830010. https://doi.org/10.1155/2022/1830010

To?açar, M., Ergen, B., & Cömert, Z. (2020). BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Medical hypotheses, 134, 109531. https://doi.org/10.1016/j.mehy.2019.109531

Ullah, N., Hassan, M., Khan, J. A., Anwar, M. S., & Aurangzeb, K. (2024). Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images. International Journal of Imaging Systems and Technology, 34(1), e23012. https://doi.org/10.1002/ima.23012

Sultan, H. H., Salem, N. M., & Al-Atabany, W. (2019). Multi-classification of brain tumor images using deep neural network. IEEE access, 7, 69215-69225. https://doi.org/10.1109/ACCESS.2019.2919122

Xu, M., Guo, L., & Wu, H. C. (2024). Novel Robust Automatic Brain-Tumor Detection and Segmentation Using Magnetic Resonance Imaging. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2024.3367123

Zaw, H. T., Maneerat, N., & Win, K. Y. (2019, July). Brain tumor detection based on Naïve Bayes Classification. In 2019 5th International Conference on engineering, applied sciences and technology (ICEAST) (pp. 1-4). IEEE. https://doi.org/10.1109/ICEAST.2019.8802562

Zhou, L., Wang, M., & Zhou, N. (2024). Distributed federated learning-based deep learning model for privacy mri brain tumor detection. arXiv preprint arXiv:2404.10026. https://doi.org/10.48550/arXiv.2404.10026

Downloads

Published

2024-12-15

How to Cite

X, F. J., Vijayakumar, M., P, S. T., P, J. S., M, E. P., & T, M. M. (2024). Breakthrough in Brain Tumor Diagnosis: A Cutting-Edge Hybrid Depthwise-Direct Acyclic Graph Network for MRI Image Classification. Journal of Applied Engineering and Technological Science (JAETS), 6(1), 730–740. https://doi.org/10.37385/jaets.v6i1.5938