The Impact of Deep Learning in Brain Tumour Analysis
Article Sidebar
Main Article Content
Abstract
The need for early and precise identification of abnormalities has made the detection and classification of brain tumours essential components of medical diagnosis. Because brain tumours are naturally complex and can have a wide range of sizes, shapes, and types, conventional diagnostic techniques like MRI interpretation and manual evaluations are difficult and time-consuming. Traditional methods frequently depend on human expertise, which is prone to errors, delays, and variability. Deep learning (DL) developments, on the other hand, have completely changed this field by providing increased automation, efficiency, and precision in tumour detection and classification because they can automatically extract pertinent features from MRI scans, Convolutional Neural Networks (CNNs) have shown impressive success in medical image analysis in recent years. CNNs improve the classification of tumour types like gliomas, meningiomas, and pituitary tumours by using multiple layers to find patterns in imaging data. Despite their efficiency, CNNs sometimes struggle with complex tumour patterns, requiring further enhancement in feature extraction. Vision Transformers (ViTs) have become a viable substitute to overcome this constraint. ViTs are especially good at identifying complex tumour structures because, in contrast to CNNs, they use self-attention mechanisms to capture global image dependencies. ViTs can perform better diagnostics by more thoroughly analysing entire MRI images. Additionally, hybrid methods that combine CNNs and ViTs have demonstrated better outcomes, taking advantage of both long-range spatial understanding (ViTs) and local feature extraction (CNNs). These developments allow for real-time medical applications, drastically improve diagnostic accuracy, and lower false positives. Neuro-oncology could undergo a revolution with the incorporation of DL models into clinical workflows, which would improve tumour detection's accuracy, speed, and accessibility. These techniques will be further developed in future studies, guaranteeing even higher accuracy and versatility in medical imaging.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
P. Chauhan et al., “Analyzing brain tumour classification techniques: A comprehensive survey,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3460380.
M. Ravinder et al., “Enhanced brain tumor classification using graph convolutional neural network architecture,” Scientific Reports, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-41407-8.
M. Aamir et al., “A deep learning approach for brain tumor classification using MRI images,” Computers and Electrical Engineering, vol. 101, Jul. 2022, doi: 10.1016/j.compeleceng.2022.108105.
R. Kaifi, “A review of recent advances in brain tumor diagnosis based on AI-Based classification,” Diagnostics, vol. 13, no. 18, Sep. 2023, doi: 10.3390/diagnostics13183007.
R. Azad et al., “Advances in medical image analysis with vision Transformers: A comprehensive review,” Medical Image Analysis, vol. 83, Jan. 2024, doi: 10.1016/j.media.2023.103000.
A. Ari and D. Hanbay, “Deep learning based brain tumor classification and detection system,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 26, no. 5, pp. 2275–2286, 2018, doi: 10.3906/elk-1801-8.
G. S. Tandel et al., “A review on a deep learning perspective in brain cancer classification,” Cancers, vol. 11, no. 1, Jan. 2019, doi: 10.3390/cancers11010111.
R. Obuchowicz, M. Strzelecki, and A. Piorkowski, “Clinical applications of artificial intelligence in medical imaging and image processing—A Review,” Cancers, vol. 16, no. 10, May 2024, doi: 10.3390/cancers16101870.
M. Aamir et al., “A deep learning approach for brain tumor classification using MRI images,” Computers and Electrical Engineering, vol. 101, Jul. 2022, doi: 10.1016/j.compeleceng.2022.108105.
J. Zhang et al., “AResU-Net: Attention residual U-Net for brain tumor segmentation,” Symmetry, vol. 12, no. 5, May 2020, doi: 10.3390/sym12050721.
N. Noreen et al., “Brain tumor classification based on fine-tuned models and the ensemble method,” Computers, Materials and Continua, vol. 67, no. 3, pp. 3967–3982, Mar. 2021, doi: 10.32604/cmc.2021.014158.
G. S. Tomar, Proceedings of the 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India: IEEE, 2023.
M. M. Ahmed et al., “Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh,” Scientific Reports, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-71893-3.
M. Buda, A. Maki, and M. A. Mazurowski, “A systematic study of the class imbalance problem in convolutional neural networks,” Neural Networks, vol. 106, pp. 249–259, Oct. 2018, doi: 10.1016/j.neunet.2018.07.011.
R. Vankdothu, M. A. Hameed, and H. Fatima, “A brain tumor identification and classification using deep learning based on CNN-LSTM method,” Computers and Electrical Engineering, vol. 101, Jul. 2022, doi: 10.1016/j.compeleceng.2022.107960.
N. Noreen et al., “Brain tumor classification based on fine-tuned models and the ensemble method,” Computers, Materials and Continua, vol. 67, no. 3, pp. 3967–3982, Mar. 2021, doi: 10.32604/cmc.2021.014158.
R. Yousef et al., “U-Net-based models towards optimal MR brain image segmentation,” Diagnostics, vol. 13, no. 9, May 2023, doi: 10.3390/diagnostics13091624.
A. Raza et al., “A hybrid deep learning-based approach for brain tumor classification,” Electronics, vol. 11, no. 7, Apr. 2022, doi: 10.3390/electronics11071146.
K. N. Rao et al., “An efficient brain tumor detection and classification using pre-trained convolutional neural network models,” Heliyon, vol. 10, no. 17, Sep. 2024, doi: 10.1016/j.heliyon.2024.e36773.
A. Barragan-Montero et al., “Artificial intelligence and machine learning for medical imaging: A technology review,” Physica Medica, vol. 83, pp. 9–24, Mar. 2021, doi: 10.1016/j.ejmp.2021.04.016.
R. Roy, B. Annappa, and S. Dodia, “3D AttU-NET for brain tumor segmentation with a novel loss function,” in 2023 6th International Conference on Information Systems and Computer Networks (ISCON), IEEE, 2023, doi: 10.1109/ISCON57294.2023.10112146.
I. D. Apostolopoulos, S. Aznaouridis, and M. Tzani, “An attention-based deep convolutional neural network for brain tumor and disorder classification and grading in Magnetic Resonance Imaging,” Information, vol. 14, no. 3, Mar. 2023, doi: 10.3390/info14030174.
A. Al-Shahrani et al., “An image processing-based and deep learning model to classify brain cancer,” Engineering, Technology and Applied Science Research, vol. 14, no. 4, pp. 15433–15438, Aug. 2024, doi: 10.48084/etasr.7803.
M. Arabahmadi, R. Farahbakhsh, and J. Rezazadeh, “Deep learning for smart healthcare—A survey on brain tumor detection from medical imaging,” Sensors, vol. 22, no. 5, Mar. 2022, doi: 10.3390/s22051960.
C. M. Umarani et al., “Advancements in deep learning techniques for brain tumor segmentation: A survey,” Informatics in Medicine Unlocked, vol. 44, 2024, doi: 10.1016/j.imu.2024.101576.
R. Yulvina et al., “Hybrid vision Transformer and convolutional neural network for multi-class and multi-label classification of tuberculosis anomalies on chest X-ray,” Computers, vol. 13, no. 12, Dec. 2024, doi: 10.3390/computers13120343.
T. D. Bui, J. Shin, and T. Moon, “3D densely convolutional networks for volumetric segmentation,” arXiv:1709.03199, Sep. 2017.
H. Chen, H. Lin, and M. Yao, “Improving the efficiency of encoder-eecoder architecture for pixel-level crack Detection,” IEEE Access, vol. 7, pp. 186657–186670, 2019, doi: 10.1109/ACCESS.2019.2961375.
B. Ahmad et al., “Brain tumor classification using a combination of variational autoencoders and generative adversarial networks,” Biomedicines, vol. 10, no. 2, Feb. 2022, doi: 10.3390/biomedicines10020223.
A. A. Akinyelu et al., “Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: A Survey,” Journal of Imaging, vol. 8, no. 8, Aug. 2022, doi: 10.3390/jimaging8080205.
A. A. Asiri et al., “Advancing brain tumor classification through fine-tuned vision transformers: A Comparative Study of Pre-Trained Models,” Sensors, vol. 23, no. 18, Sep. 2023, doi: 10.3390/s23187913.
B. A. G. et al., “Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach,” BMC Medical Imaging, vol. 24, no. 1, Dec. 2024, doi: 10.1186/s12880-024-01323-3.
H. Chen, H. Lin, and M. Yao, “Improving the efficiency of encoder-decoder architecture for pixel-level crack detection,” IEEE Access, vol. 7, pp. 186657–186670, 2019, doi: 10.1109/ACCESS.2019.2961375.
C. W. Lin, Y. Hong, and J. Liu, “Aggregation-and-attention network for brain tumor segmentation,” BMC Medical Imaging, vol. 21, no. 1, Dec. 2021, doi: 10.1186/s12880-021-00639-8.
E. K. Rutoh et al., “GAIR-U-Net: 3D guided attention inception residual u-net for brain tumor segmentation using multimodal MRI images,” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 6, Jul. 2024, doi: 10.1016/j.jksuci.2024.102086.
S. Solanki et al., “Brain tumor detection and classification using intelligence techniques: An Overview,” IEEE Access, vol. 11, pp. 12870–12886, 2023, doi: 10.1109/ACCESS.2023.3242666.
M. K. Abd-Ellah et al., “Design and implementation of a computer-aided diagnosis system for brain tumor classification,” in 2016 28th International Conference on Microelectronics (ICM), IEEE, 2016.
P. K. Mall et al., “A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities,” Healthcare Analytics, vol. 4, Dec. 2023, doi: 10.1016/j.health.2023.100216.
A. Khatami et al., “Convolutional neural network for medical image classification using wavelet features,” in 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020.
A. Biswas et al., “Data augmentation for improved brain tumor segmentation,” IETE Journal of Research, vol. 69, no. 5, pp. 2772–2782, 2023, doi: 10.1080/03772063.2021.1905562.
A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv:2010.11929, 2020.
A. Amarnath, A. Al Bataineh, and J. A. Hansen, “Transfer-Learning approach for enhanced brain tumor classification in MRI imaging,” BioMedInformatics, vol. 4, no. 3, pp. 1745–1756, Jul. 2024, doi: 10.3390/biomedinformatics4030094.