Vol. 2 No. 2 (2024): Issue - 02
Articles

A Detailed Review on Brain Image Segmentation using Deep Learning Algorithms

DOWNLOAD PDF
  • Mayur B Suvarna,
  • Tarun Gonal,
  • Gowri B M,
  • Mubarak Ali
Mayur B Suvarna
School of Computer Science and Engineering, REVA University, Bangalore, 560064, India
Tarun Gonal
School of Computer Science and Engineering, REVA University, Bangalore, 560064, India
Gowri B M
School of Computer Science and Engineering, REVA University, Bangalore, 560064, India
Mubarak Ali
School of Computer Science and Engineering, REVA University, Bangalore, 560064, India
DOI: https://doi.org/10.5281/zenodo.14566235

Published 2024-12-28

Keywords

  • Brain tumor segmentation,
  • Deep learning techniques,
  • U-Net architecture,
  • Self-attention mechanisms,
  • MRI image analysis

How to Cite

Mayur B Suvarna, Tarun Gonal, Gowri B M, & Mubarak Ali. (2024). A Detailed Review on Brain Image Segmentation using Deep Learning Algorithms. Milestone Transactions on Medical Technometrics, 2(2), 88–105. https://doi.org/10.5281/zenodo.14566235

Abstract

The need for a better method of diagnosis is essential, as evidenced by the increasing incidence of accurate brain tumour identification in the field of neuro-oncology. The existing literature, which is mainly concerned with the classification of MRI images, does not provide thorough answers to the many problems that arise in brain tumour segmentation, including imaging abnormalities, the difficult-to-define boundaries of tumours, tumour heterogeneity, and classification uncertainties. By putting forth a novel deep learning framework that blends the well-known U-Net architecture with self-attention processes, specially tailored for brain tumour segmentation, this study seeks to overcome these problems. Our work thoroughly evaluates and contrasts current deep learning methods, highlighting the efficiency of the U-Net architecture in recognizing both particular and generic patterns in three-dimensional brain imaging. Key findings show that our proposed model outperforms recent advances in brain tumour segmentation from 2020 to 2024 in terms of accuracy, precision, sensitivity, and specificity. Significant results suggest that this combination of factors sets a new standard in medical image segmentation, with the potential to revolutionize diagnostic capabilities and therapeutic approaches. The implications go beyond academic discussion, giving patients and healthcare professionals hope for the accurate diagnosis and management of brain tumours. The integration of self-attention mechanisms has proven effective in improving segmentation accuracy by focusing on critical tumour regions and improving overall precision.

DOWNLOAD PDF

References

  1. Al-Shammari, N. K., Syed, T. H., & Syed, M. B. (2021). An Edge–IoT framework and prototype based on blockchain for smart healthcare applications. Engineering, Technology & Applied Science Research, 11(4), 7326-7331.
  2. Alagarsamy, S., Govindaraj, V., & A, S. (2023). Automated brain tumor segmentation for MR brain images using artificial bee colony combined with interval type-II fuzzy technique. IEEE Transactions on Industrial Informatics, 19(11), 11150–11159. https://doi.org/10.1109/TII.2023.3244344
  3. Alzahrani, S. M. (2023). ConvAttenMixer: Brain tumor detection and type classification using convolutional mixer with external and self-attention mechanisms. In Journal of King Saud University - Computer and Information Sciences (Vol. 35, Issue 10, p. 101810). Elsevier BV. https://doi.org/10.1016/j.jksuci.2023.101810
  4. Amara, K., Guerroudji, M. A., Kerdjidj, O., Zenati, N., & Ramzan, N. (2023). HOLOTumor: 6 DoF phantom head pose estimation-based deep learning and brain tumor segmentation for AR visualization and interaction. IEEE Sensors Journal, 23(19), 23367–23376. https://doi.org/10.1109/JSEN.2023.3305596
  5. Amin, J., Sharif, M., Haldorai, A., Yasmin, M., & Nayak, R. S. (2021). Brain tumor detection and classification using machine learning: a comprehensive survey. In Complex & Intelligent Systems (Vol. 8, Issue 4, pp. 3161–3183). Springer Science and Business Media LLC. https://doi.org/10.1007/s40747-021-00563-y
  6. Anaya-Isaza, A., Mera-Jiménez, L., & Fernandez-Quilez, A. (2023). CrossTransUnet: A new computationally inexpensive tumor segmentation model for brain MRI. IEEE Access, 11, 27066–27085. https://doi.org/10.1109/ACCESS.2023.3257767
  7. BJ, S., Seema, S., & Rohith, S. (2024). A Visual Computing Unified Application Using Deep Learning and Computer Vision Techniques. International Journal of Interactive Mobile Technologies, 18(1).
  8. Brahim, I., Fourer, D., Vigneron, V., & Maaref, H. (2019). Deep Learning Methods for MRI Brain Tumor Segmentation: a comparative study. In 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1–6). 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE. https://doi.org/10.1109/ipta.2019.8936077
  9. Chaithra, G., Shrinivas, P. A., Naidu, B. G., Vinay, G. S., Supreeth, S., & Biradar, A. (2024, August). Emotion Detection using Deep Learning. In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON) (pp. 1-6). IEEE.
  10. Chen, H., An, J., Jiang, B., Xia, L., Bai, Y., & Gao, Z. (2023). WS-MTST: Weakly supervised multi-label brain tumor segmentation with transformers. IEEE Journal of Biomedical and Health Informatics, 27(12), 5914–5925. https://doi.org/10.1109/JBHI.2023.3321602
  11. Chen, H., Qin, Z., Ding, Y., Tian, L., & Qin, Z. (2020). Brain tumor segmentation with deep convolutional symmetric neural network. In Neurocomputing (Vol. 392, pp. 305–313). Elsevier BV. https://doi.org/10.1016/j.neucom.2019.01.111
  12. Chitnis, S., Hosseini, R., & Xie, P. (2022). Brain tumor classification based on neural architecture search. In Scientific Reports (Vol. 12, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41598-022-22172-6
  13. Ding, Y., et al. (2023). RLSegNet: A medical image segmentation network based on reinforcement learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(4), 2565–2576. https://doi.org/10.1109/TCBB.2022.3195705
  14. Ejaz, K., Suaib, N. B. M., Kamal, M. S., Rahim, M. S. M., & Rana, N. (2023). Segmentation method of deterministic feature clustering for identification of brain tumor using MRI. IEEE Access, 11, 39695–39712. https://doi.org/10.1109/ACCESS.2023.3263798
  15. El-Assiouti, O. S., Hamed, G., El-Saadawy, H., Ebied, H. M., & Khattab, D. (2023). RegionInpaint, Cutoff and RegionMix: Introducing novel augmentation techniques for enhancing the generalization of brain tumor identification. IEEE Access, 11, 83232–83250. https://doi.org/10.1109/ACCESS.2023.3301873
  16. Fajar, A., Sarno, R., Fatichah, C., Susilo, R. I., & Pangestu, G. (2023). Cyclical learning rate optimization on deep learning model for brain tumor segmentation. IEEE Access, 11, 119802–119810. https://doi.org/10.1109/ACCESS.2023.3326475
  17. Farzamnia, A., Hazaveh, S. H., Siadat, S. S., & Moung, E. G. (2023). MRI brain tumor detection methods using contourlet transform based on time-adaptive self-organizing map. IEEE Access, 11, 113480–113492. https://doi.org/10.1109/ACCESS.2023.3322450
  18. Fathima, S. N., Rekha, K. B., Safinaz, S., & Ahmed, S. T. (2024). Computational techniques, classification, datasets review and way forward with modern analysis of epileptic seizure–a study. Multimedia Tools and Applications, 1-17.
  19. Gu, Y., Guan, Y., Yu, Z., & Dong, B. (2023). SegCoFusion: An integrative multimodal volumetric segmentation cooperating with fusion pipeline to enhance lesion awareness. IEEE Journal of Biomedical and Health Informatics, 27(12), 5860–5871. https://doi.org/10.1109/JBHI.2023.3318131
  20. Harahap, M., Husein, A. M., Deol, S. S., Singh, S., Situmorang, S. D. P., & Saputra, J. (2022). Comparative Analysis of Deep Learning Approach for Detection and Segmentation of Brain Tumor. In 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM) (pp. 01–05). 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM). IEEE. https://doi.org/10.1109/icosnikom56551.2022.10034876
  21. Hou, Q., Peng, Y., Wang, Z., Wang, J., & Jiang, J. (2023). MFD-net: Modality fusion diffractive network for segmentation of multimodal brain tumor image. IEEE Journal of Biomedical and Health Informatics, 27(12), 5958–5969. https://doi.org/10.1109/JBHI.2023.3318640
  22. Huang, N.-Y., & Liu, C.-X. (2024). Efficient Tumor Detection and Classification Model Based on ViT in an End-to-End Architecture. In IEEE Access (Vol. 12, pp. 106096–106106). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/access.2024.3424294
  23. Hyder, M. S., Vijeth, J., Sushma, S., Bawzir, M. K., & Supreeth, S. (2020). Power Aware Virtual Machine Migration for Resource Allocation in Cloud. Test Magzine, May-June 2020 ISSN: 0193-4120 Page No. 5212-5216
  24. 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
  25. Jalalifar, S. A., Soliman, H., Sahgal, A., & Sadeghi-Naini, A. (2023). Automatic assessment of stereotactic radiation therapy outcome in brain metastasis using longitudinal segmentation on serial MRI. IEEE Journal of Biomedical and Health Informatics, 27(6), 2681–2692. https://doi.org/10.1109/JBHI.2023.3235304
  26. Jayashree, J., Vijayashree, J., Iyengar, N. C. S., & Ahmed, S. T. (2022). Artificial Neural Networks with Enhanced Nearest Neighbour algorithm for Heart Disease Prediction. International Journal of Human Computations & Intelligence, 1(4), 1-9.
  27. Jia, Z., & Chen, D. (2024). Brain Tumor Identification and Classification of MRI images using deep learning techniques. In IEEE Access (pp. 1–1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/access.2020.3016319
  28. Krishnamurthy, K. T., Rohith, S., Basavaraj, G. M., Swathi, S., & Supreeth, S. (2023, June). Design and Development of Walking Monitoring System for Gait Analysis. In International Conference on Multi-disciplinary Trends in Artificial Intelligence (pp. 475-483). Cham: Springer Nature Switzerland.
  29. Kumar, A., Satheesha, T. Y., Salvador, B. B. L., Mithileysh, S., & Ahmed, S. T. (2023). Augmented Intelligence enabled Deep Neural Networking (AuDNN) framework for skin cancer classification and prediction using multi-dimensional datasets on industrial IoT standards. Microprocessors and Microsystems, 97, 104755.
  30. Li, X., Fang, X., Yang, G., Su, S., Zhu, L., & Yu, Z. (2023). TransU2-Net: An effective medical image segmentation framework based on transformer and U2-Net. IEEE Journal of Translational Engineering in Health and Medicine, 11, 441–450. https://doi.org/10.1109/JTEHM.2023.3289990
  31. Li, Z., Kamnitsas, K., Ouyang, C., Chen, C., & Glocker, B. (2023). Context label learning: Improving background class representations in semantic segmentation. IEEE Transactions on Medical Imaging, 42(6), 1885–1896. https://doi.org/10.1109/TMI.2023.3242838
  32. Lin, J., et al. (2023). CKD-TransBTS: Clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation. IEEE Transactions on Medical Imaging, 42(8), 2451–2461. https://doi.org/10.1109/TMI.2023.3250474
  33. Liu, Z., Tong, L., Chen, L., Jiang, Z., Zhou, F., Zhang, Q., Zhang, X., Jin, Y., & Zhou, H. (2022). Deep learning based brain tumor segmentation: a survey. In Complex & Intelligent Systems (Vol. 9, Issue 1, pp. 1001–1026). Springer Science and Business Media LLC. https://doi.org/10.1007/s40747-022-00815-5
  34. Magadza, T., & Viriri, S. (2021). Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art. Journal of Imaging, 7(2), 19. https://doi.org/10.3390/jimaging7020019
  35. Magadza, T., & Viriri, S. (2023). Efficient nnU-net for brain tumor segmentation. IEEE Access, 11, 126386–126397. https://doi.org/10.1109/ACCESS.2023.3329517
  36. Mallampati, B., Ishaq, A., Rustam, F., Kuthala, V., Alfarhood, S., & Ashraf, I. (2023). Brain tumor detection using 3D-UNet segmentation features and hybrid machine learning model. IEEE Access, 11, 135020–135034. https://doi.org/10.1109/ACCESS.2023.3337363
  37. Metlek, S., & Çetiner, H. (2023). ResUNet+: A new convolutional and attention block-based approach for brain tumor segmentation. IEEE Access, 11, 69884–69902. https://doi.org/10.1109/ACCESS.2023.3294179
  38. Mitra, S. (2023). Deep learning with radiogenomics towards personalized management of gliomas. IEEE Reviews in Biomedical Engineering, 16, 579–593. https://doi.org/10.1109/RBME.2021.3075500
  39. Mundada, M. R., Sowmya, B. J., Supreeth, S., Prabhu, S. G., Mahesh, K., Vishwanath, Y., & Rohith, S. (2024). Skin Cancer Prediction by Incorporating Bio-inspired Optimization in Deep Neural Network. SN Computer Science, 5(8), 1127.
  40. Neamah, K., et al. (2024). Brain tumor classification and detection-based DL models: A systematic review. IEEE Access, 12, 2517–2542. https://doi.org/10.1109/ACCESS.2023.3347545
  41. Nguyen-Tat, T. B., Nguyen, T.-Q. T., Nguyen, H.-N., & Ngo, V. M. (2024). Enhancing brain tumor segmentation in MRI images: A hybrid approach using UNet, attention mechanisms, and transformers. In Egyptian Informatics Journal (Vol. 27, p. 100528). Elsevier BV. https://doi.org/10.1016/j.eij.2024.100528
  42. Nigam, A., Srivastava, A., Singh, A., & Behera, A. K. (2023). Recognizing phishing site using Machine Learning-A Comparative Approach using MultinomialNB & Logistic Regression. Journal of Advanced Zoology, 44.
  43. Özkaya, Ç., & Sağiroğlu, S. (2023). Glioma grade classification using CNNs and segmentation with an adaptive approach using histogram features in brain MRIs. IEEE Access, 11, 52275–52287. https://doi.org/10.1109/ACCESS.2023.3273532
  44. Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. In IEEE Transactions on Medical Imaging (Vol. 35, Issue 5, pp. 1240–1251). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tmi.2016.2538465
  45. Preetha, R., Priyadarsini, M. J. P., & Nisha, J. S. (2023). Comparative study on architecture of deep neural networks for segmentation of brain tumor using magnetic resonance images. IEEE Access, 11, 138549–138567. https://doi.org/10.1109/ACCESS.2023.3340443
  46. Rajendran, S., et al. (2023). Automated segmentation of brain tumor MRI images using deep learning. IEEE Access, 11, 64758–64768. https://doi.org/10.1109/ACCESS.2023.3288017
  47. Rajitha, K., Kavya, S. M., & Supreeth, S. (2020). EFFICIENT VIRTUAL MACHINE LOAD BALANCING USING CLOUD COMPUTING ENVIRONMENT, International Journal of Advanced Research in Computer Science, Volume 11, Special Issue I, May 2020
  48. Ramachandran, M., Patan, R., Kumar, A., Hosseini, S., & Gandomi, A. H. (2023). Mutual informative MapReduce and minimum quadrangle classification for brain tumor big data. IEEE Transactions on Engineering Management, 70(8), 2644–2655. https://doi.org/10.1109/TEM.2021.3073018
  49. Ramamoorthy, H., Ramasundaram, M., Raj, R. S. P., & Randive, K. (2023). TransAttU-Net deep neural network for brain tumor segmentation in magnetic resonance imaging. IEEE Canadian Journal of Electrical and Computer Engineering, 46(4), 298–309. https://doi.org/10.1109/ICJECE.2023.3289609
  50. Ramprasad, M. V. S., Rahman, M. Z. U., & Bayleyegn, M. D. (2023). SBTC-net: Secured brain tumor segmentation and classification using black widow with genetic optimization in IoMT. IEEE Access, 11, 88193–88208. https://doi.org/10.1109/ACCESS.2023.3304343
  51. Renugadevi, M., et al. (2023). Machine learning empowered brain tumor segmentation and grading model for lifetime prediction. IEEE Access, 11, 120868–120880. https://doi.org/10.1109/ACCESS.2023.3326841
  52. Rohith, S., Bhargavi, S., Basavaraj, G. M., Sreenivasulu, K. N., Chavan, P., & Supreeth, S. (2023, October). Image Encryption and Decryption using Symmetric Key Sequence of Elliptic Curve (EC) Over Prime Field. In 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) (pp. 1-5). IEEE.
  53. Rohith, S., Jahnavi, L., Bhuvaneshwari, S. C., Supreeth, S., & Sujatha, B. K. (2020, December). Image encryption and decryption using key sequence of triple logistic map for medical applications. In 2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC) (pp. 1-5). IEEE.
  54. Roy, S., Saha, R., Sarkar, S., Mehera, R., Pal, R. K., & Bandyopadhyay, S. K. (2023). Brain tumour segmentation using S-Net and SA-net. IEEE Access, 11, 28658–28679. https://doi.org/10.1109/ACCESS.2023.3257722
  55. Saraei, M., & Liu, S. (2023). Attention-based Deep Learning Approaches in Brain Tumor Image Analysis: A Mini Review. In Frontiers in Health Informatics (Vol. 12, p. 164). Farname, Inc. https://doi.org/10.30699/fhi.v12i0.493
  56. Sathiyamoorthi, V., Ilavarasi, A. K., Murugeswari, K., Ahmed, S. T., Devi, B. A., & Kalipindi, M. (2021). A deep convolutional neural network based computer aided diagnosis system for the prediction of Alzheimer's disease in MRI images. Measurement, 171, 108838.
  57. Shah, S. M. A. H., et al. (2023). Classifying and localizing abnormalities in brain MRI using channel attention-based semi-Bayesian ensemble voting mechanism and convolutional auto-encoder. IEEE Access, 11, 75528–75545. https://doi.org/10.1109/ACCESS.2023.3294562
  58. Shetty, C., Seema, S., Sowmya, B. J., Nandalike, R., Supreeth, S., P, D., ... & Goud, V. (2024). A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy. Journal of Engineering, 2024(1), 2893021.
  59. Shi, J., Yu, L., Cheng, Q., Yang, X., Cheng, K.-T., & Yan, Z. (2024). MFTrans: Modality-masked fusion transformer for incomplete multi-modality brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 28(1), 379–390. https://doi.org/10.1109/JBHI.2023.3326151
  60. Shruthi, G., Mundada, M. R., Sowmya, B. J., & Supreeth, S. (2022). Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing. In S. El Kafhali (Ed.), Applied Computational Intelligence and Soft Computing (Vol. 2022, pp. 1–17). Hindawi Limited. https://doi.org/10.1155/2022/2131699
  61. Shruthi, G., Mundada, M. R., Supreeth, S., & Gardiner, B. (2023). Deep learning-based resource prediction and mutated leader algorithm enabled load balancing in fog computing. International Journal of computer networks and information security, 15(4), 84-95.
  62. Shruthi, G., Mundada, M., & Supreeth, S. (2022). The resource allocation using weighted greedy knapsack based algorithm in an educational fog computing environment. International Journal of Emerging Technologies in Learning (iJET), 17(18), 261-274.
  63. Shu, X., et al. (2023). Deep slice-crossed network with local weighted loss for brain metastases segmentation. IEEE Transactions on Cognitive and Developmental Systems, 15(3), 1419–1429. https://doi.org/10.1109/TCDS.2022.3213944
  64. 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
  65. Soomro, T. A., et al. (2023). Image segmentation for MR brain tumor detection using machine learning: A review. IEEE Reviews in Biomedical Engineering, 16, 70–90. https://doi.org/10.1109/RBME.2022.3185292
  66. Sowmya, B., Alex, S. A., Kanavalli, A., Supreeth, S., Shruthi, G., & Rohith, S. (2024). Machine learning model for emotion detection and recognition using an enhanced convolutional neural network. Journal of Integrated Science and Technology, 12(4), 786-786.
  67. Sunagar, P., Sowmya, B. J., Pruthviraja, D., Supreeth, S., Mathew, J., Rohith, S., & Shruthi, G. (2024). Hybrid RNN Based Text Classification Model for Unstructured Data. SN Computer Science, 5(6), 726.
  68. Supreeth S., Patil Kirankumari, “VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment”. (2022). In KSII Transactions on Internet and Information Systems (Vol. 16, Issue 6). Korean Society for Internet Information (KSII). https://doi.org/10.3837/tiis.2022.06.007
  69. Supreeth, S., & Biradar, S. (2013). Scheduling virtual machines for load balancing in cloud computing platform. International Journal of Science and Research (IJSR), 2(6), 437-441.
  70. Supreeth, S., & Patil, K. (2022). Hybrid genetic algorithm and modified-particle swarm optimization algorithm (GA-MPSO) for predicting scheduling virtual machines in educational cloud platforms. International Journal of Emerging Technologies in Learning (iJET), 17(7), 208-225.
  71. Supreeth, S., & Patil, K. K. (2019). Virtual machine scheduling strategies in cloud computing-A review. International Journal on Emerging Technologies, 10(3), 181-188.
  72. Supreeth, S., & Raja Rajeshwari, M. M. (2018). Using map-reduce for image analysis in cloud environment. In Proceedings of International Conference on Cognition and Recognition: ICCR 2016 (pp. 109-118). Springer Singapore.
  73. Supreeth, S., & Zamri, N. E. (2024). Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter. Applied Computational Intelligence & Soft Computing, 2024.
  74. Supreeth, S., Bhargavi, S., Margam, R., Annaiah, H., & Nandalike, R. (2023). Virtual Machine Placement Using Adam White Shark Optimization Algorithm in Cloud Computing. SN Computer Science, 5(1), 21.
  75. Supreeth, S., Patil, K., Patil, S. D., & Rohith, S. (2022, July). Comparative approach for VM scheduling using modified particle swarm optimization and genetic algorithm in cloud computing. In 2022 IEEE International Conference on Data Science and Information System (ICDSIS) (pp. 1-6). IEEE.
  76. Supreeth, S., Patil, K., Patil, S. D., Rohith, S., Vishwanath, Y., & Prasad, K. S. V. (2022). An Efficient Policy-Based Scheduling and Allocation of Virtual Machines in Cloud Computing Environment. In N. Pasquino (Ed.), Journal of Electrical and Computer Engineering (Vol. 2022, pp. 1–12). Hindawi Limited. https://doi.org/10.1155/2022/5889948
  77. Theakstone, A. G., Brennan, P. M., Jenkinson, M. D., Mills, S. J., Syed, K., Rinaldi, C., Xu, Y., Goodacre, R., Butler, H. J., Palmer, D. S., Smith, B. R., & Baker, M. J. (2021). Rapid Spectroscopic Liquid Biopsy for the Universal Detection of Brain Tumours. In Cancers (Vol. 13, Issue 15, p. 3851). MDPI AG. https://doi.org/10.3390/cancers13153851
  78. Ting, H., & Liu, M. (2024). Multimodal transformer of incomplete MRI data for brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 28(1), 89–99. https://doi.org/10.1109/JBHI.2023.3286689
  79. Tran, M.-T., Yang, H.-J., Kim, S.-H., & Lee, G.-S. (2023). Prediction of survival of glioblastoma patients using local spatial relationships and global structure awareness in FLAIR MRI brain images. IEEE Access, 11, 37437–37449. https://doi.org/10.1109/ACCESS.2023.3266771
  80. Ullah, F., et al. (2023). Evolutionary model for brain cancer grading and classification. IEEE Access, 11, 126182–126194. https://doi.org/10.1109/ACCESS.2023.3330919
  81. Usha, M. G., Shreya, M. S., Supreeth, S., Shruthi, G., Pruthviraja, D., & Chavan, P. (2024, July). Kidney Tumor Detection Using MLflow, DVC and Deep Learning. In 2024 Second International Conference on Advances in Information Technology (ICAIT) (Vol. 1, pp. 1-7). IEEE.
  82. Vatanpour, M., & Haddadnia, J. (2023). TransDoubleU-Net: Dual scale swin transformer with dual level decoder for 3D multimodal brain tumor segmentation. IEEE Access, 11, 125511–125518. https://doi.org/10.1109/ACCESS.2023.3330958
  83. Vinay, A. N., Vidyasagar, K. N., Rohith, S., Supreeth, S., Prasad, S. N., Kumar, S. P., & Bharathi, S. H. (2024). Dysfluent Speech Classification Using Variational Mode Decomposition and Complete Ensemble Empirical Mode Decomposition Techniques with NGCU based RNN. IEEE Access.
  84. Vinay, N. A., Vidyasagar, K. N., Rohith, S., Dayananda, P., Supreeth, S., & Bharathi, S. H. (2024). An RNN-Bi LSTM based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) from Heart Beat Sound: A Feature Optimization Process. IEEE Access.
  85. Walsh, J., Othmani, A., Jain, M., & Dev, S. (2022). Using U-Net network for efficient brain tumor segmentation in MRI images. In Healthcare Analytics (Vol. 2, p. 100098). Elsevier BV. https://doi.org/10.1016/j.health.2022.100098
  86. Wang, J., et al. (2023). SDPN: A slight dual-path network with local-global attention guided for medical image segmentation. IEEE Journal of Biomedical and Health Informatics, 27(6), 2956–2967. https://doi.org/10.1109/JBHI.2023.3260026
  87. Wang, X., et al. (2023). A segmentation framework with unsupervised learning-based label mapper for the ventricular target of intracranial germ cell tumor. IEEE Journal of Biomedical and Health Informatics, 27(11), 5381–5392. https://doi.org/10.1109/JBHI.2023.3310492
  88. Wisaeng, K. (2023). U-Net++DSM: Improved U-Net++ for brain tumor segmentation with deep supervision mechanism. IEEE Access, 11, 132268–132285. https://doi.org/10.1109/ACCESS.2023.3331025
  89. Xu, M.-C., et al. (2023). MisMatch: Calibrated segmentation via consistency on differential morphological feature perturbations with limited labels. IEEE Transactions on Medical Imaging, 42(10), 2988–2999. https://doi.org/10.1109/TMI.2023.3273158
  90. Yang, H., Sun, J., & Xu, Z. (2023). Learning unified hyper-network for multi-modal MR image synthesis and tumor segmentation with missing modalities. IEEE Transactions on Medical Imaging, 42(12), 3678–3689. https://doi.org/10.1109/TMI.2023.3301934
  91. Yang, H., Zhou, T., Zhou, Y., Zhang, Y., & Fu, H. (2023). Flexible fusion network for multi-modal brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 27(7), 3349–3359. https://doi.org/10.1109/JBHI.2023.3271808
  92. Younis, A., Li, Q., Khalid, M., Clemence, B., & Adamu, M. J. (2023). Deep learning techniques for the classification of brain tumor: A comprehensive survey. IEEE Access, 11, 113050–113063. https://doi.org/10.1109/ACCESS.2023.3317796
  93. Zaitoon, R., & Syed, H. (2023). RU-Net2+: A deep learning algorithm for accurate brain tumor segmentation and survival rate prediction. IEEE Access, 11, 118105–118123. https://doi.org/10.1109/ACCESS.2023.3325294
  94. Zhang, J., Jiang, Z., Dong, J., Hou, Y., & Liu, B. (2020). Attention Gate ResU-Net for Automatic MRI Brain Tumor Segmentation. In IEEE Access (Vol. 8, pp. 58533–58545). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/access.2020.2983075
  95. Zhang, X., et al. (2023). Self-supervised tumor segmentation with Sim2Real adaptation. IEEE Journal of Biomedical and Health Informatics, 27(9), 4373–4384. https://doi.org/10.1109/JBHI.2023.3240844
  96. Zhao, J., et al. (2023). Uncertainty-aware multi-dimensional mutual learning for brain and brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 27(9), 4362–4372. https://doi.org/10.1109/JBHI.2023.3274255
  97. Zhuang, Y., Liu, H., Song, E., & Hung, C.-C. (2023). A 3D cross-modality feature interaction network with volumetric feature alignment for brain tumor and tissue segmentation. IEEE Journal of Biomedical and Health Informatics, 27(1), 75–86. https://doi.org/10.1109/JBHI.2022.3214999
  98. ZongRen, L., Silamu, W., Yuzhen, W., & Zhe, W. (2023). DenseTrans: Multimodal brain tumor segmentation using swin transformer. IEEE Access, 11, 42895–42908. https://doi.org/10.1109/ACCESS.2023.3272055