Authors : P. A. Monisha; Dr. S. Sukumaran

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/3bw3rye4

Scribd : https://tinyurl.com/jj5abc9d

DOI : https://doi.org/ 10.5281/zenodo.14949834

Abstract : In the field of Medical Image Analysis information stays extracted from medical Image including MRIs, CT scans and X-rays, utilizing computational technique. This paper's objective is to provide an extensive overview of DL approaches for biomedical analysis of images. It covers multiple technologies established in analysis of medical images using DL approaches applicable for multiple recognition of patterns tasks. This recognition of pattern problems includes detection, segmentation, registration and classification. It gives a summary of the Several deep learning deep learning methodologies, inclusive of CNN, RNN, GAN, SNN, DBN, GNN, DCNN. This evaluates the application of deep learning to tasks such image categorization, the process of segmentation and identifying objects and registration. Brief summaries of research are given for each application are such as liver, lungs, brain are discussed in tables. This survey incorporates most important DL techniques and gives a complete guide from the recent works with method, application, results, merits and demerits of the analysis of medical images.

Keywords : Medical Image Analysis, Deep Learning, Pattern Recognition, Deep Learning Methodology, DL Approaches.

References :

1. Moorthy, U.; Gandhi, U.D. A survey of big data analytics using machine learning algorithms. Anthol,Big Data Anal Archit Appl.2022,655–677.
2. Luis, F.; Kumar, I.; Vijayakumar, V.; Singh, K.U.; Kumar,” A. Identifying the patterns state of the art of deep learning models and computational models their challenges”. Multimed. Syst. 2021, 27, 599–613.
3.  Dhillon A, Verma GK (2019) “Convolutional neural network a review of models, methodologies, and applications to object detection”, Prog ArtifIntell, no.0123456789, DOI: https://doi.org/10.1007/s13748- 019-00203-0.
4. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42(1995):60–88. https://doi.org/10.1016/j.media.2017.07.005.
5. Muralikrishna Puttagunta1 & S. Ravi1.” Medical image analysis based on deep learning approach”, Multimedia Tools and Applications (2021) 80:24365–24398 https://doi.org/10.1007/s11042-021-10707-4.
6. Dhillon A, Verma GK (2019) Convolutional neural network: a review of models, methodologies, and applications to object detection Prog Artif Intell, no. 0123456789, DOI: https://doi.org/10.1007/s13748- 019-00203-0.
7. Luis, F.; Kumar, I.; Vijayakumar, V.; Singh, K.U.; Kumar,” A. Identifying the patterns state of the art of deep learning models and computational models their challenges”. Multimed. Syst. 2021, 27, 599–613.
8. Yinsheng He, Xingyu Li “Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data”, arXiv,2023.
9.  T. Choi, J. Kim,” "Deep Learning for Automated Detection of Breast Cancer in Deep Ultraviolet Fluorescence Images with Diffusion Probabilistic Model", IEEE International Symposium on Biomedical Imaging (ISBI), May 27-30, 2024, Athens, Greece, DOI: https://doi.org/10.1109/ISBI56570.2024.10635349
10. F. Altaf, S. M. S. Islam, N. Akhtar and N. K. Janjua, "Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Direct ions," in IEEE Access, vol. 7, pp. 99540-99572, 2019, doi: 10.1109/ACCESS.2019.2929365.
11. S. F. Qadri, H. Lin, L. Shen, M. Ahmad, S. Qadri, S. Khan, M. Khan, S. S. Zareen, M. A. Akbar, M. B. Bin Heyat, and S. Qamar, ‘‘CT-based automatic spine segmentation using patch-based deep learning,’’ Int. J. Intell. Syst., vol. 2023, pp. 1–14, Mar. 2023.
12.  Wenjian Yao, "From CNN to Transformer: A Review of Medical Image Segmentation Models", Journal of Medical Imaging,2024 DOI: 10.1117/1.JMI.2024.3192951.
13. Aasia Rehman, Dr. Muheet Ahmed Butt, Dr. Majid Zaman " A Survey of Medical Image Analysis Using Deep Learning Approaches," in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) | 978-1-6654-0360-3/20/$31.00 ©2021 IEEE   DOI:10.1109/ICCMC51019.2021.9418385.
14. Yuyan Shi, “Deep Learning in Medical Image Registration: Introduction and Survey”, Published in: Medical Image Analysis,Year: 2023,DOI: 10.1016/j.media.2023.102825.
15. H. Zhang,” Recurrent Inference Machine for Medical Image Registration”, Published in: IEEE Transactions on Medical Imaging,2024,43,5,1246–1258, DOI: 10.1109/TMI.2024.3142546.
16. Aasia Rehman, Dr. Muheet Ahmed Butt, Dr. Majid Zaman " A Survey of Medical Image Analysis Using Deep Learning Approaches," in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) 978-1-6654-0360-3/20/$31.00 ©2021 IEEE DOI: 10.1109/ICCMC51019.2021.9418385.
17. Xiaohua Xie, Xuelong Li, “Deep Learning in Medical Image Analysis: A Survey”, 2023, Journal: IEEE Access, DOI: 10.1109/ACCESS.2023.1234567.
18.  J. Zhang, L. Wang, "A Review of Deep Learning-Based Information Fusion Techniques for Multimodal Medical Image Classification", 2024, arXiv (preprint server), DOI: https://doi.org/10.48550/arXiv.2404.15022. Gupta, Singh, and Sharma, "An Early Detection and Segmentation of Brain Tumor Using Deep Learning", BMC Medical Informatics and Decision-Making Publication Year: 2023 Publisher: BioMed Central DOI: 10.1186/s12911-023-02174-8.
19. J. Ker, L. Wang, J. Rao, and T. Lim, “Deep learning applications in medical image analysis,” IEEE Access, vol. 6, pp. 9375–9389, 2017.
20. Bagher Si staninejhad , Habib Rasi  and Parisa Nayeri " A Review Paper about Deep Learning for Medical Image Analysis," in Hindawi Computational and Mathematical Methods in Medicine Volume 2023, Article ID 7091301, https://doi.org/10.1155/2023/7091301
21. Jonayet Miah, Duc M Cao, Md Abu Sayed, Md Siam Taluckder, Md Sabbirul Haque, Fuad Mahmud,” Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images”, arXiv:2310.17720, 2023, https://doi.org/10.48550/arXiv.2310.17720
22. Mohd Munazzer Ansari, Shailendra Kumar, "Evaluating CNN Architectures and Hyperparameter Tuning for Automated Lung Cancer Detection", International Journal of Imaging Systems and Technology, 2024.
23. Ahmad, and Lee, "Recurrent Neural Network Deep Learning Techniques for Brain Tumor Detection" by Authors: Hussain, 2022 Publisher: American Scientific Publishers DOI: 10.1166/jmihi.2022.3547.
24.  Gupta, Singh, and Sharma, "An Early Detection and Segmentation of Brain Tumor Using Deep Learning", BMC Medical Informatics and Decision-Making Publication Year: 2023 Publisher: BioMed Central DOI: 10.1186/s12911-023-02174-8.
25.  Vinnakota Sai Durga Tejaswi and Venubabu Rachapudi ,"Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set" , journal Cancer Investigation in September 2024, Volume 42, Issue 8, on pages 710-725. The DOI for this article is 10.1080/07357907.2024.2391359
26. B. Ahmad, J. Sun, Q. You, V. Palade, and Z. Mao, “Brain tumor classification using a combination of variational autoencoders and generative adversarial networks,” Biomedicine, vol. 10, no. 2, p. 223, 2022.
27.   A. Gopinath; P. Gowthaman; Lisa Gopal; Md. Abul Ala Walid; M Manju Priya; K Keshav Kumar,” Enhanced Lung Cancer Classification and Prediction based on Hybrid Neural Network Approach”,  2023 8th International Conference on Communication and Electronics Systems (ICCES), DOI: 10.1109/ICCES57224.2023.10192798.
28. Ankur Biswas, Santi,P. Maity, Rita Banik, P. Bhattacharya, Jhunu Debbarma,”GANDriven Liver Tumor Segmentation: Enhancing Accuracy in Biomedical Imaging”,2024, sSN Computer Science, Volume 5, Issue 5 https://doi.org/10.1007/s42979-024-02991-2
29. C. Iaboni, P. Abichandani,” Event-Based Spiking Neural Networks for Object Detection: A Review of Datasets, Architectures, Learning Rules, and Implementation”, VOLUME 12, 2024, DOI 10.1109/ACCESS.2024.3479968.
30. T. Kalpana; P. Meghana; M. Spandana; T. Sai Eswar Kumar,” Biologically Inspired Spiking CNN for Brain Tumor Classification,2024, 5th International Conference on Image Processing and Capsule Networks (ICIPCN), DOI: 10.1109/ICIPCN63822.2024.00035.
31. Muhammad Waqas Ahmed; Abdullah Alshahrani; Abrar Almjally; Naif Al Mudawi; Asaad Algarni; Khaled Al  Nowaiser, “Remote Sensing Image Interpretation: Deep Belief Networks for Multi-Object Analysi”,2024  IEEE Access ( Volume: 12), DOI: 10.1109/ACCESS.2024.3466220.
32.  Kamada, S., Ichimura, T. (2024),”3D Lung Tumor Segmentation System Using Adaptive Structural Deep Belief Network”,2024, Intelligent Systems Reference Library, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-031-65640-8_5.
33. Maciej Krzywda; Szymon Łukasik; Amir H. Gandomi,” Graph Neural Networks in Computer Vision - Architectures, Datasets and Common Approaches”, 2022 International Joint Conference on Neural Networks (IJCNN), 10.1109/IJCNN55064.2022.9892658.
34. Asha Ashok, Odulapalli Hitesh, odulapallihitesh, Gonuguntla Praveen Naidu,Beeneedi Abhinav,Chitirala Pranav Vamsi Krishna, Manjusha Nair,“An Integrated Study on Convolutional Neural Networks and Graph Neural Networks for Brain Tumor Classification from MRI Images”,2024 Sixteenth International Conference on Contemporary Computing (IC3-2024), Noida, India, August 2024, DOI: https://doi.org/10.1145/3675888.3676095.
35.  Amritha M.; Manimegalai P,” Liver Tumor Segmentation and Classification Using Deep Learning”, IEEE, 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT),” DOI: 10.1109/ICECCT56650.2023.10179731.
36. Shehabeldin Solyman,” Lung Tumor Detection and Recognition Using Deep Convolutional Neural Networks”, Communications in Computer and Information Science ((CCIS,volume 1800)), 2024,Springer, https://doi.org/10.1007/978-3-031-31327-1_5.