ADVANCEMENTS IN TRANSFER LEARNING: A COMPREHENSIVE REVIEW OF NOVEL APPROACHES FOR MRI BRAIN IMAGE DIAGNOSIS
DOI:
https://doi.org/10.25271/sjuoz.2025.13.3.1470Keywords:
Transfer Learning, machine learning, MRI Image, Diagnose Diseases, Training Algorithm, Deep learningAbstract
Magnetic Resonance Imaging (MRI) has rapidly advanced and established itself as an indispensable tool in both the detection and diagnosis of several diseases, most notably brain tumors. The interpretation of MRI scans still largely relies on expert radiologists, which can be time-consuming and potentially subject to variability. Transfer learning (henceforth, TL) approaches show potential for improving diagnostic precision in medical imaging analysis. In this literature review, the potential of MRI scans in classifying and detecting various medical conditions, such as glioma and Alzheimer’s, is discussed alongside current algorithmic limitations. Current research indicates potential challenges in adapting existing supervised deep learning algorithms that process MRI images to more efficient approaches. The findings suggest a notable increase in the quality of detecting sub-pathologies, even with a scarcity of well-annotated images. This can potentially reduce the training cycle duration. When transfer learning is applied to diagnostic approaches, it may act as supplemental support for decision-making processes for tumorous growth detection, potentially reducing the time period for treatment and increasing effectiveness according to preliminary research. This review examines the expansion in transfer learning in MRI for the assessment and treatment of brain disorders through recent algorithms from the current literature.
References
Abdulqadir, M., & Abdulazeez, A. M. (2024). A Review on Alzheimer’s Disease Classification Using Deep Learning. Indonesian Journal of Computer Science, 13. https://doi.org/10.33022/ijcs.v13i3.4031
Al-Azzwi, Z. (2024). Medical image Classification using Transfer Learning: Convolutional Neural Network Models approach. Journal of Electrical Systems, 20, 2561–2569. https://doi.org/10.52783/jes.3243
Ali, A. H., & Abdulazeez, A. M. (2024). Transfer Learning in Machine Learning: A Review of Methods and Applications. Indonesian Journal of Computer Science. https://api.semanticscholar.org/CorpusID:271605825
Alla, S., & Athota, K. (2022). Brain Tumor Detection Using Transfer Learning in Deep Learning. Indian Journal Of Science And Technology, 15, 2093–2102. https://doi.org/10.17485/IJST/v15i40.1307
Ashraf, A., Qingjie, Z., Bangyal, W. H. K., & Iqbal, M. (2024). Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things. IEEE Transactions on Consumer Electronics, 70(1), 4478–4489. https://doi.org/10.1109/TCE.2023.3328479
Azeez, O., & Abdulazeez, A. (2024). Classification of Brain Tumor based on Machine Learning Algorithms: A Review. Journal of Applied Science and Technology Trends, 6, 01–15. https://doi.org/10.38094/jastt61188
Bibi, N., Wahid, F., Ma, Y., Ali, S., Abbasi, I. A., Alkhayyat, A., & Khyber. (2024). A Transfer Learning-Based Approach for Brain Tumor Classification. IEEE Access, 12, 111218–111238. https://doi.org/10.1109/ACCESS.2024.3425469
Combs, K., Lu, H., & Bihl, T. J. (2023). Transfer Learning and Analogical Inference: A Critical Comparison of Algorithms, Methods, and Applications. Algorithms, 16(3). https://doi.org/10.3390/a16030146
Dhakshnamurthy, V. K., Govindan, M., Sreerangan, K., Nagarajan, M. D., & Thomas, A. (2024). Brain Tumor Detection and Classification Using Transfer Learning Models. Engineering Proceedings, 62(1). https://doi.org/10.3390/engproc2024062001
Disci, R., Gurcan, F., & Soylu, A. (2025). Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models. Cancers, 17(1). https://doi.org/10.3390/cancers17010121
Gopinadhan, A. (2024). Ad-Tl: Alzheimer’s Disease Prediction Using Transfer Learning. Journal of Electrical Systems, 20, 1132–1147. https://doi.org/10.52783/jes.2845
Gu, C. (2024). Enhancing medical image classification with convolutional neural networks through transfer learning: A comprehensive review. Applied and Computational Engineering, 35, 280–284. https://doi.org/10.54254/2755-2721/35/20230407
Kaifi, R. (2023). A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification. Diagnostics, 13, 3007. https://doi.org/10.3390/diagnostics13183007
Khaliki, M. Z., & Başarslan, M. S. (2024). Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN. Scientific Reports, 14(1), 2664. https://doi.org/10.1038/s41598-024-52823-9
Kittani, T., & Abdulazeez, A. (2024). Deep Learning Classification Algorithms Applications: A Review. Indonesian Journal of Computer Science, 13. https://doi.org/10.33022/ijcs.v13i3.4064
Mahmud, T., Barua, K., Habiba, S. U., Sharmen, N., Hossain, M. S., & Andersson, K. (2024). An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning. Diagnostics, 14(3). https://doi.org/10.3390/diagnostics14030345
Matsoukas, C., Haslum, J. F., Sorkhei, M., Soderberg, M. P., & Smith, K. (2022). What Makes Transfer Learning Work for Medical Images: Feature Reuse & Other Factors. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9215–9224. https://doi.org/10.1109/CVPR52688.2022.00901
Meena, M., Balaswetha, U., & Harini, M. (2024). Brain Tumor Classification Using Pretained Deep Convolutional Neural Networks. International Journal of Health Sciences and Research. https://api.semanticscholar.org/CorpusID:269688172
Mohammed, A.-Z., Ansarullah, S., Al-Eissa, M., Dar, G., Alqahtani, R., & Alkahtani, S. (2024). Exploring the Efficacy of Deep Learning Techniques in Detecting and Diagnosing Alzheimer’s Disease: A Comparative Study. Journal of Disability Research, 3. https://doi.org/10.57197/JDR-2024-0064
Muthuraj, S. (2024). Neural Networks in Neuroimaging: A Critical Analysis of Deep Learning Techniques for Brain Tumor Prediction. Journal of Electrical Systems, 20, 1617–1636. https://doi.org/10.52783/jes.1468
Nag, A., Mondal, H., Mehedi Hassan, M., Al-Shehari, T., Kadrie, M., Al-Razgan, M., Alfakih, T., Biswas, S., & Kumar Bairagi, A. (2024). TumorGANet: A Transfer Learning and Generative Adversarial Network- Based Data Augmentation Model for Brain Tumor Classification. IEEE Access, 12, 103060–103081. https://doi.org/10.1109/ACCESS.2024.3429633
Natha, S., Laila, U., Gashim, I. A., Mahboob, K., Saeed, M. N., & Noaman, K. M. (2024). Automated Brain Tumor Identification in Biomedical Radiology Images: A Multi-Model Ensemble Deep Learning Approach. Applied Sciences, 14(5). https://doi.org/10.3390/app14052210
Nayak, R., Rao, S., Jayakar Shetty, S., S, V., & Shashikala. (2024). Brain Tumor Detection Using Deep Learning Technique. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 12(V). https://doi.org/10.22214/ijraset.2024.61745
Pal, A., Seth, J., Tyagi, A., Haider, A., & Mishra, A. (2024). An Efficient Brain Tumor Detection Using Ensemble Learning. Educational Administration Theory and Practices, 30. https://doi.org/10.53555/kuey.v30i5.4686
Panigrahi, S., Adhikary, D., & Pattanayak, B. (2024). Analyzing Activation Functions With Transfer Learning-Based Layer Customization for Improved Brain Tumor Classification. IEEE Access, PP, 1–1. https://doi.org/10.1109/ACCESS.2024.3497346
Raza, S., Gul, N., Khattak, H., Rehan, A., Farid, M., Kamal, A., Rajput, D., Mukhtiar, S., & Ullah, A. (2024). BRAIN TUMOR DETECTION AND CLASSIFICATION USING DEEP FEATURE FUSION AND STACKING CONCEPTS. Journal of Population Therapeutics and Clinical Pharmacology, 1339–1356. https://doi.org/10.53555/jptcp.v31i1.4179
Rebar, Z., & Abdulazeez, A. (2024). Deep and Machine Learning Algorithms for Diagnosing Brain Cancer and Tumors. Indonesian Journal of Computer Science, 13, 3932–3960. https://doi.org/10.33022/ijcs.v13i3.4028
Reddy, L., Muniyandy, E., Vamsikrishna, M., & Ravindra, C. (2024). Brain Tumor Detection and Classification Using Deep Learning Models on MRI Scans. EAI Endorsed Transactions on Pervasive Health and Technology, 10. https://doi.org/10.4108/eetpht.10.5553
Ren, T., Honey, E., Rebala, H., Sharma, A., Chopra, A., & Kurt, M. (2024). An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation. ArXiv, abs/2402.07008. https://api.semanticscholar.org/CorpusID:267626912
Salehi, A. W., Khan, S., Gupta, G., Alabduallah, B. I., Almjally, A., Alsolai, H., Siddiqui, T., & Mellit, A. (2023). A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope. Sustainability, 15(7). https://doi.org/10.3390/su15075930
Sedeeq, N., & Abdulazeez, A. M. (2024). CNN-Based Segmentation and Detection of Brain Tumors MRI Images: A Review. Indonesian Journal of Computer Science, 13. https://doi.org/10.33022/ijcs.v13i3.4029
Shamshad, N., Sarwr, D., Almogren, A., Saleem, K., Munawar, A., Rehman, A., & Bharany, S. (2024). Enhancing Brain Tumor Classification by a Comprehensive Study on Transfer Learning Techniques and Model Efficiency Using MRI Datasets. IEEE Access, PP, 1–1. https://doi.org/10.1109/ACCESS.2024.3430109
Srikrishna, M., Seo, W., Zettergren, A., Kern, S., Cantré, D., Gessler, F., Sotoudeh, H., Seidlitz, J., Bernstock, J. D., Wahlund, L.-O., Westman, E., Skoog, I., Virhammar, J., Fällmar, D., & Schöll, M. (2024). Assessing CT-based Volumetric Analysis via Transfer Learning with MRI and Manual Labels for Idiopathic Normal Pressure Hydrocephalus. medRxiv. https://doi.org/10.1101/2024.06.23.24309144
Ullah, M. S., Khan, M. A., Masood, A., Mzoughi, O., Saidani, O., & Alturki, N. (2024). Brain tumor classification from MRI scans: A framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm. Frontiers in Oncology, 14. https://doi.org/10.3389/fonc.2024.1335740
Ullah, Z., Jamjoom, M., Thirumalaisamy, M., Alajmani, S., Saleem, F., Sheikh Akbari, A., & Khan, U. (2024). A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor. Biomedical Engineering and Computational Biology, 15. https://doi.org/10.1177/11795972241277322
Sorour, E. S, Amr A. A., Khalied M. A., Abdulrahman K. A., and Abeer A. W. (2024). Deep Learning Classification using MRI for Alzheimer’s Disease Detection. International Scientific Journal of Engineering and Management. https://api.semanticscholar.org/CorpusID:269453742
Vajiram, J., & Senthil, A. (2024). Brain MRI detection by Sematic Segmentation models- Transfer Learning approach. https://arxiv.org/abs/2405.14886
Wageh, M., Amin, K., Algarni, A., & Hamad, A. (2024). Brain Tumor Detection Based on Deep Features Concatenation and Machine Learning Classifiers With Genetic Selection. IEEE Access, 12, 114923–114939. https://doi.org/10.1109/ACCESS.2024.3446190
Zhou, Y., Zhang, X., Wang, Y., & Zhang, B. (2021). Transfer learning and its application research. Journal of Physics: Conference Series, 1920, 012058. https://doi.org/10.1088/1742-6596/1920/1/012058
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2020). A Comprehensive Survey on Transfer Learning. Proceedings of the IEEE, PP, 1–34. https://doi.org/10.1109/JPROC.2020.3004555
Zubair Rahman, A. M. J., Gupta, M., Aarathi, S., Mahesh, T. R., Vinoth Kumar, V., Yogesh Kumaran, S., & Guluwadi, S. (2024). Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering. BMC Medical Informatics and Decision Making, 24(1), 113. https://doi.org/10.1186/s12911-024-02519-x
PANDIYAN, M., JENISHA, E., MADHURANJANI, G., & BRINDHA DHARSHINI, S. (2024). BRAIN TUMOR DETECTION USING DEEP TRANSFER LEARNING. International Journal of Scientific Research in Engineering and Management (IJSREM), 08(03). https://doi.org/10.55041/IJSREM29704
Mehmood, Y., & Bajwa, U. I. (2024). Brain tumor grade classification using the ConvNext architecture. DIGITAL HEALTH, 10, 20552076241284920. https://doi.org/10.1177/20552076241284920
Hsu, S., Lin, M.-H., Lin, C.-F., Hsiao, T.-Y., Wang, Y.-M., & Sun, C.-W. (2024). Brain tumor grading diagnosis using transfer learning based on optical coherence tomography. Biomedical Optics Express, 15. https://doi.org/10.1364/BOE.513877
Shchetinin, E. Y. (2024). Brain tumor segmentation by deep learning transfer methods using MRI images. Computer Optics, 48(3), 439–444. https://doi.org/10.18287/2412-6179-CO-1366
Zia-ur-Rehman, Awang, M. K., Rashid, D.-J., Ghulam, A., Hamid, D., Mahmoud, S., Saleh, D., & Ahmad, D. (2024). Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique. PLOS ONE, 19. https://doi.org/10.1371/journal.pone.0304995
Sorour, S. E., El-Mageed, A. A. A., Albarrak, K. M., Alnaim, A. K., Wafa, A. A., & El-Shafeiy, E. (2024). Classification of Alzheimer’s disease using MRI data based on Deep Learning Techniques. Journal of King Saud University - Computer and Information Sciences, 36(2), 101940. https://doi.org/10.1016/j.jksuci.2024.101940
Raina, D., Dawange, A., Bandha, T., Kaur, A., Wasekar, R., Verma, K., Verma, S., & Dhingra, K. (2024). Convoluted neural network and transfer learning algorithm for improved brain tumor classifications in MRI. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (Online), 3, 200–212. https://doi.org/10.60087/jklst.v3.n4.p200
Rustom, F., Moroze, E., Parva, P., Ogmen, H., & Yazdanbakhsh, A. (2024). Deep learning and transfer learning for brain tumor detection and classification. Biology Methods and Protocols, 9(1), bpae080. https://doi.org/10.1093/biomethods/bpae080
Zhou, J. (2024). Deep learning applications in MRI for brain tumor detection and image segmentation. Applied and Computational Engineering, 33, 42–48. https://doi.org/10.54254/2755-2721/33/20230229
Shedbalkar, J., & Prabhushetty, K. (2024). Deep transfer learning model for brain tumor segmentation and classification using UNet and chopped VGGNet. Indonesian Journal of Electrical Engineering and Computer Science, 33, 1405. https://doi.org/10.11591/ijeecs.v33.i3.pp1405-1415
Bhardwaj, N., Sood, M., & gill, sandeep. (2024). Design of Transfer Learning based Deep CNN Paradigm for Brain Tumor Classification. WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE, 21, 162–169. https://doi.org/10.37394/23208.2024.21.17
Ravikumar, K., Sahoo, S., Mandhavi, B., Mohan, V., Babu, G., & Panigrahi, B. (2024). Detection of Brain Tumour based on Optimal Convolution Neural Network. EAI Endorsed Transactions on Pervasive Health and Technology, 10. https://doi.org/10.4108/eetpht.10.5464
Murugesan, S., B V, B., D, P., S, A., & Kumar M, S. (2024). Efficient brain tumor grade classification using ensemble deep learning models. BMC Medical Imaging, 24. https://doi.org/10.1186/s12880-024-01476-1
Kumar M, S., Sonaimuthu, S., Murugesan, S., Rajadurai, H., & Shivahare, B. (2024). Employing deep learning and transfer learning for accurate brain tumor detection. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-57970-7
Ali, M., Kim, K., Khalid, M., Farrash, M., Zafar, A., & Lee, S. (2024). Enhancing Alzheimer’s disease diagnosis and staging: A multistage CNN framework using MRI. Frontiers in Psychiatry, 15. https://doi.org/10.3389/fpsyt.2024.1395563
Naveen, N., & Nagaraj, G. C. (2024). Enhancing Early Alzheimer’s Disease Detection: Leveraging Pre-trained Networks and Transfer Learning. International Journal of Intelligent Systems and Applications(IJISA), 16(1), 52–69. https://doi.org/10.5815/ijisa.2024.01.05
Shah, Dr. D. (2024). Impact of Dimensionality Reduction Method in Brain Tumor Classification Using Transfer Learning. Journal of Electrical Systems, 20, 1470–1480. https://doi.org/10.52783/jes.5320
Albalawi, E., T R, M., Thakur, A., Kumar, V. V., Gupta, M., Bhatia, S., & Almusharraf, A. (2024). Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor. BMC Medical Imaging, 24. https://doi.org/10.1186/s12880-024-01261-0
Khaw, L. W., & Abdullah, S. S. (2024). MRI Brain Image Classification Using Convolutional Neural Networks And Transfer Learning. Journal of Advanced Research in Computing and Applications, 31(1), 20–26. https://doi.org/10.37934/arca.31.1.2026
Rao, B., Aparna, M., Kolisetty, S. S., Janapana, H., & Koteswararao, Y. (2024). Multi-class Classification of Alzheimer’s Disease Using Deep Learning and Transfer Learning on 3D MRI Images. Traitement Du Signal, 41, 1397–1404. https://doi.org/10.18280/ts.410328
Kako, N. A., Abdulazeez, A. M., & Abdulqader, D. N. (2024). Multi-label deep learning for comprehensive optic nerve head segmentation through data of fundus images. Heliyon, 10(18), e36996. https://doi.org/10.1016/j.heliyon.2024.e36996
Gottipati, S., & Thumbur, G. (2024). Multi-modal fusion deep transfer learning for accurate brain tumor classification using magnetic resonance imaging images. Indonesian Journal of Electrical Engineering and Computer Science, 34, 825. https://doi.org/10.11591/ijeecs.v34.i2.pp825-834
Alotaibi, S., Rehman, A., Raza, A., Alyami, J., & Saba, T. (2024). CVG-Net: Novel transfer learning based deep features for diagnosis of brain tumors using MRI scans. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.2008
Kilani, S., Aghili, S. N., Fathi, Y., & Sburlea, A. I. (2024). Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces. Frontiers in Neuroscience, 18. https://doi.org/10.3389/fnins.2024.1360709
Neamah, K., Mohamed, F., Kurdi, W., Yaseen, A., & Kadhim, K. (2024). Utilizing Deep improved ResNet50 for Brain Tumor Classification Based MRI. IEEE Open Journal of the Computer Society, PP, 1–12. https://doi.org/10.1109/OJCS.2024.3453924
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