Optimized Brain Tumor Detection: A Dual- Module Approach for MRI Image Enhancement and Tumor Classification
Keywords:
Brain Tumor, MRI, MobileNet, Deep Learning, Accuracy, RobustAbstract
The images and their segmentations obtained from the MR images are critical in making an early diagnosis and treatment of brain tumors. In this project, an attempt is made to develop a highly advanced automatic framework employing deep learning neural architectures for classification and segmentation of tumors based on MobileNet and UNET as the two main underlying architectures enhancing accuracy and computational efficiency in detection. In other words, the good feature of MobileNet is that it is light in design and offers a real-time implementation possibility when the performance itself is based on a model that is less complex. On the other hand, classifies anatomically-specific features across its densely connected layers for maximum accuracy and robustness. The system will be responsible for classifying MRI brain images into tumors and non-tumors. The classification networks are designed on MobileNet and U-net architectures to maximize accuracy and minimize computing power. MobileNet offers an optimized lightweight architecture suitable for edge and mobile implementations, with fast inference, while it improves detection accuracy with good gradient flow. The framework can also be extended to include segmentation methods to localize the tumor sites in the human brain. The integration of such a model would push the diagnostic capacity of an automated, reliable, and accurate tumor detection system to support clinical decision- making, thus having the potential to enhance the diagnosis while reducing invasion techniques. From here, one could foresee the advancement to real-time diagnostic systems in clinical hospitals. The proposed methods will be validated on benchmark datasets, with the performance metrics for validation in real medical imaging scenarios taking accuracy, precision, recall, and segmentation quality as their goals.
Downloads
References
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 234–241). Springer.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv preprint arXiv:1704.04861.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700–4708.
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., ... & van lewelyn, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BraTS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024.
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2019). No New-Net. In International MICCAI Brainlesion Workshop, 234–244.
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., ... & Summers, R. M. (2016). Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298.
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251.
Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., ... & Glocker, B. (2017). Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation. Medical Image Analysis, 36, 61–78.
Talo, M., Baloglu, U. B., Yildirim, O., & Acharya, U. R. (2019). Application of Deep Transfer Learning for Automated Brain Abnormality Classification Using MR Images. Cognitive Systems Research, 54, 176–188.
Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., & Vercauteren, T. (2018). Aleatoric Uncertainty Estimation with Test-Time Augmentation for Medical Image Segmentation with Convolutional Neural Networks. Neurocomputing, 338, 34–45.
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., ... & Larochelle, H. (2017). Brain Tumor Segmentation with Deep Neural Networks. Medical Image Analysis, 35, 18–31.
Zhao, Z., & Jia, X. (2019). Deep Feature Learning with Discriminative Localization for Fine-Grained Brain Tumor Classification. IEEE Access, 7, 67382–67391.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., ... & van der Laak, J. A. (2017). A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42, 60–88.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929.
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In 3D Vision (3DV), IEEE, 565–571.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
