Deep Learning Applications in Medical Image Analysis: U-Net for Diagnosis


  • Shanthi H J, Hamza Mohammed Ridha Al-Khafaji, Salwan Mohammed Shaheed, Rajasekhar Pittala, Harendra Singh Negi, Arun Pratap Srivastava, Navneet Kumar, Anurag Shrivastava


medical image analysis, U-Net, segmentation, deep learning, diagnostic accuracy.


This research investigates the application of U-Net engineering in restorative image investigation for enhanced symptomatic capabilities. Leveraging a different dataset comprising MRI, CT scans, and X-rays, we methodically compare U-Net with conventional CNN, SegNet, and state-of-the-art DeepLabv3. The U-Net show showcases predominant execution, accomplishing a Dice coefficient of 0.85, an Intersection over Union (IoU) of 0.75, and a pixel exactness of 0.92. The incorporation of skip associations in U-Net demonstrates instrumental in protecting spatial data, driving more exact division comes about. Moreover, our examination amplifies to particular therapeutic conditions, illustrating U-Net's flexibility with a Dice coefficient of 0.87 for tumor division and 0.83 for organ outline. The results confirm U-Net as a vigorous and dependable instrument for exact medical picture division, with suggestions for improved demonstrative precision over different imaging modalities.


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ALEKSEEVA, V., NECHYPORENKO, A., FROHME, M., GARGIN, V., MENIAILOV, I. and CHUMACHENKO, D., 2023. Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation. Electronics, 12(5), pp. 1202.

ÁLVARO, Y.R., CAVAGNARO, M. and CROCCO, L., 2023. An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications. Sensors, 23(2), pp. 643.

BUSCHI, D., CURTI, N., COLA, V., CARLINI, G., SALA, C., DANIELE DALL’OLIO, CASTELLANI, G., PIZZI, E., SARA, D.M., FOGLIA, A., GIUNTI, M., PISONI, L. and GIAMPIERI, E., 2023. Automated Wound Image Segmentation: Transfer Learning from Human to Pet via Active Semi-Supervised Learning. Animals, 13(6), pp. 956.

CHAE, J. and KIM, J., 2023. An Investigation of Transfer Learning Approaches to Overcome Limited Labeled Data in Medical Image Analysis. Applied Sciences, 13(15), pp. 8671.

CHEE, C.L., LING, A.H.W., YEN, F.C., MOHD, Y.M., ALSHANTTI, K. and AZIZ, M.E., 2023. Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture. Diagnostics, 13(14), pp. 2377.

[6] CHEN, Z., 2023. Medical Image Segmentation Based on U-Net. Journal of Physics: Conference Series, 2547(1), pp. 012010.

CUI, H., HU, L. and LING, C., 2023. Advances in Computer-Aided Medical Image Processing. Applied Sciences, 13(12), pp. 7079.

CUI, R., WANG, L., LIN, L., LI, J., LU, R., LIU, S., LIU, B., GU, Y., ZHANG, H., SHANG, Q., CHEN, L. and TIAN, D., 2023. Deep Learning in Barrett’s Esophagus Diagnosis: Current Status and Future Directions. Bioengineering, 10(11), pp. 1239.

ELLOUMI, N., SLIM, B.C., SEDDIK, H. and NADRA, T., 2023. A 3D Processing Technique to Detect Lung Tumor. International Journal of Advanced Computer Science and Applications, 14(6),.

GAJULA, S. and RAJESH, V., 2023. Deep Learning based Analysis of MRI Images for Brain Tumor Diagnosis. International Journal of Advanced Computer Science and Applications, 14(2),.

GALIĆ, I., HABIJAN, M., LEVENTIĆ, H. and ROMIĆ, K., 2023. Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods. Electronics, 12(21), pp. 4411.

HOSSEINI, F., ASADI, F., EMAMI, H. and EBNALI, M., 2023. Machine learning applications for early detection of esophageal cancer: a systematic review. BMC Medical Informatics and Decision Making, 23, pp. 1-17.

HU, M., ZHANG, J., MATKOVIC, L., LIU, T. and YANG, X., 2023. Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions. Journal of Applied Clinical Medical Physics, 24(2),.

HUSSAIN, S., LAFARGA-OSUNA, Y., MANSOOR, A., NASEEM, U., AHMED, M. and TAMEZ-PEÑA, J.G., 2023. Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review. BMC Bioinformatics, 24, pp. 1-21.

JIANG, X., HU, Z., WANG, S. and ZHANG, Y., 2023. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers, 15(14), pp. 3608.

JIN, S., LIU, G. and BAI, Q., 2023. Deep Learning in COVID-19 Diagnosis, Prognosis and Treatment Selection. Mathematics, 11(6), pp. 1279.

Bani Ahmad, A. Y. A. ., Kumari, D. K. ., Shukla, A. ., Deepak, A. ., Chandnani, M. ., Pundir, S. ., & Shrivastava, A. . (2023). Framework for Cloud Based Document Management System with Institutional Schema of Database. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 672–678.

P. William, Anurag Shrivastava, Upendra Singh Aswal, Indradeep Kumar, Framework for Implementation of Android Automation Tool in Agro Business Sector, 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 10.1109/ICIEM59379.2023.10167328

P. William, Anurag Shrivastava, Venkata Narasimha Rao Inukollu, Viswanathan Ramasamy, Parul Madan, Implementation of Machine Learning Classification Techniques for Intrusion Detection System, 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 10.1109/ICIEM59379.2023.10167390

N Sharma, M Soni, S Kumar, R Kumar, N Deb, A Shrivastava, Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market, ACM Transactions on Asian and Low-Resource Language Information Processing.

Ajay Reddy Yeruva, Esraa Saleh Alomari, S Rashmi, Anurag Shrivastava, Routing in Ad Hoc Networks for Classifying and Predicting Vulnerabilities, Cybernetics and Systems, Taylor & Francis, 2023

P William, OJ Oyebode, G Ramu, M Gupta, D Bordoloi, A Shrivastava, Artificial intelligence based models to support water quality prediction using machine learning approach, 2023 International Conference on Circuit Power and Computing Technologie

J Jose, A Shrivastava, PK Soni, N Hemalatha, S Alshahrani, CA Saleel, An analysis of the effects of nanofluid-based serpentine tube cooling enhancement in solar photovoltaic cells for green cities, Journal of Nanomaterials 2023

K Murali Krishna, Amit Jain, Hardeep Singh Kang, Mithra Venkatesan, Anurag Shrivastava, Sitesh Kumar Singh, Muhammad Arif, Deelopment of the Broadband Multilayer Absorption Materials with Genetic Algorithm up to 8 GHz Frequency, Security and Communication Networks

P Bagane, SG Joseph, A Singh, A Shrivastava, B Prabha, A Shrivastava, Classification of malware using Deep Learning Techniques, 2021 9th International Conference on Cyber and IT Service Management (CITSM).

A Shrivastava, SK Sharma,Various arbitration algorithm for onchip (AMBA) shared bus multi-processor SoC, 2016 IEEE Students' Conference on Electrical, Electronics and Computer Science, SCEECS 509330

Gandomi, M. Haider, “Beyond the hype: Big data concepts, methods, and analytics”, International Journal of Information Management, vol. 35, no. 2, pp. 137-144, 2015.

Shrivastava, A., Chakkaravarthy, M., Shah, M.A..A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches. In Cybernetics and Systems, 2022

Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 100219

Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–729

Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321

Boina, R., Ganage, D., Chincholkar, Y.D., .Chinthamu, N., Shrivastava, A., Enhancing Intelligence Diagnostic Accuracy Based on Machine Learning Disease Classification. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 765–774

Shrivastava, A., Pundir, S., Sharma, A., ...Kumar, R., Khan, A.K. Control of A Virtual System with Hand Gestures. In Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, 2023, pp. 1716–1721

P. Srivastava, P. Choudhary, S. A. Yadav, A. Singh and S. Sharma, A System for Remote Monitoring of Patient Body Parameters, International Conference on Technological Advancements and Innovations (ICTAI), 2021, pp. 238-243,




How to Cite

Shanthi H J, Hamza Mohammed Ridha Al-Khafaji, Salwan Mohammed Shaheed, Rajasekhar Pittala, Harendra Singh Negi, Arun Pratap Srivastava, Navneet Kumar, Anurag Shrivastava. (2024). Deep Learning Applications in Medical Image Analysis: U-Net for Diagnosis. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 494–500. Retrieved from



Research Article