International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Artificial Intelligence in Medical Image Analysis |
|---|---|
| ABSTRACT | Artificial Intelligence (AI) technology has been found to be a game-changer in the field of medical image analysis, which significantly increases the accuracy, efficiency, and reliability of disease diagnosis. With the advent of medical imaging modalities, including X-rays, Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, and ultrasound, there is a need for developing intelligent systems that can aid healthcare professionals in analyzing complex medical data. This literature survey aims to present the current advancements in AI-based techniques, including Machine Learning (ML) and Deep Learning (DL), for analyzing medical images. The Deep Learning techniques, especially Convolutional Neural Networks (CNNs), have shown remarkable results in analyzing medical images. These techniques have been used to diagnose different diseases, including cancer, brain tumors, and COVID-19, thus increasing the accuracy of disease diagnosis and reducing human errors. The paper presents a literature survey based on different research works published between 2020 and 2025. Additionally, the study highlights some of the challenges faced in the process, which include the need for a significant dataset, the computational cost, and the issue of data privacy and interpretability. The study further highlights some of the possible directions that may be followed in the future, which include the application of AI in real-time healthcare systems, the development of interpretable AI systems, and the improvement of personalized medicine. The survey highlights the significance of AI in the analysis of medical images and its potential in revolutionizing healthcare in the near future. |
| AUTHOR | M. KRISHNA KUMAR, C. BALASUNDAR, S.G. ABARNA, S. VARSHA, N. MUNISELVAM Assistant Professor, Department of Mathematics, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India Assistant Professor, Department of Information Technology, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India UG Student, Department of Artificial Intelligence and Data Science, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403087 |
| pdf/87_Artificial Intelligence in Medical Image Analysis.pdf | |
| KEYWORDS | |
| References | 1. Chan, H.-P., Samala, R. K., Hadjiiski, L. M., & Zhou, C. (2020). Deep Learning in Medical Image Analysis. Advances in Experimental Medicine and Biology . DOI: https://doi.org/10.1007/978-3-030-33128-3_1 2. Singh, S. P., Wang, L., Gupta, S., et al. (2020). 3D Deep Learning on Medical Images: A Review. Sensors, 20(18), 5097. DOI: https://doi.org/10.3390/s20185097 3. Mondal, M. R. H., Bharati, S., & Podder, P. (2021). Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review. DOI: https://doi.org/10.48550/arXiv.2110.14910 4. Saw, S. N., & Ng, K. H. (2022). Current Challenges of Implementing Artificial Intelligence in Medical Imaging. Physica Medica . DOI: https://doi.org/10.1016/j.ejmp.2022.06.003 5. Galić, I., Habijan, M., Leventić, H., & Romić, K. (2023). Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods. Electronics, 12(21), 4411. DOI: https://doi.org/10.3390/electronics12214411 6. M. Karuppasamy, M. Jansi Rani, and M. Prabha, An Efficient Resource Allocation Mechanism Using Intelligent Scheduling for Managing Energy in Cloud Computing Infrastructure, Information and Communication Technology for Competitive Strategies (ICTCS 2021), Lecture Notes in Networks and Systems 401 (2023), 81-86. 7. Metaheuristic Feature Selection for Diabetes Prediction with P-G-S Approach, Karuppasamy M, Jansi Rani M, Poorani K, 4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, Procedia Computer Science 252 (2025) 165–171. 8. M. Karuppasamy, M. Prabha, and M. Jansi Rani, Future Worth: Predicting Resale Values with Machine Learning Techniques, Inventive Communication and Computational Technologies, Lecture Notes in Networks and Systems 757 (2023), 1101-1112. 9. M. Jansi Rani, M. Karuppasamy, M. Prabha, and K. Pooran, Detection of COVID-19 CoronaVirus Using ResNet Deep Learning Technique, Signal Processing, Telecommunication and Embedded Systems with AI and ML Applications, Lecture Notes in Electrical Engineering 1281 (2025), 71-83. 10. A Review of Medical Artificial Intelligence. (2020). Global Health Journal, 4(2), 44–63. DOI: https://doi.org/10.1016/j.glohj.2020.04.002 11. Teng, Z., Li, L., Xin, Z., et al. (2024). A Literature Review of Artificial Intelligence for Medical Image Segmentation: From AI to Trustworthy AI. Quantitative Imaging in Medicine and Surgery. DOI: https://doi.org/10.21037/qims-24-723 12. Szilágyi, L., & Kovács, L. (2024). Artificial Intelligence Technology in Medical Image Analysis. Applied Sciences, 14(5), 2180. DOI: https://doi.org/10.3390/app14052180 13. Anusha, K., Pallerla, N., & Bachina, L. P. (2025). AI Revolutionizing Diagnostic Imaging: Enhancing Accuracy and Efficiency. International Journal of Medical Informatics Research. DOI: https://doi.org/10.58806/ijmir.2025.v2i10n01 14. A Five-Year Systematic Review of AI-Based Medical Image Analysis. (2025). Array. DOI: https://doi.org/10.1016/j.array.2025.100320 |
