Artificial Intelligence (AI) in Medical Image-Pneumonia and Bone Fracture Detection

Medical X-ray image interpretation requires considerable clinical expertise and may become challenging when the workload is high or access to experienced medical professionals is limited. This study presents a web-based medical image analysis application developed using the Django framework for the automated classification of pneumonia from chest X-ray images and bone fractures from skeletal X-ray images. The proposed system integrates deep learning-based image classification with a web application interface, allowing users to upload X-ray images and obtain model-generated predictions through a single platform. The application also incorporates image validation, prediction-result storage, and an AI-assisted explanation module using the Google Gemini API to present the prediction in a more understandable form. The system was developed to demonstrate the practical integration of deep learning models into an accessible medical imaging workflow rather than to replace professional clinical diagnosis. The application provides a structured workflow from image submission and preprocessing to model inference and presentation of the predicted result. The study also discusses the limitations associated with image quality, dataset diversity, classification coverage, and the absence of explicit lesion or fracture localization. The proposed system demonstrates how a web-based deep learning application can be used as a supportive tool for preliminary medical image assessment and as a platform for further research in AI-assisted medical imaging.

Artificial Intelligence predictions, Medical Image, Convolutional Neural Network, Django, PostgreSQL, HTML5, CSS3, Bootstrap, Javascript, JSON API

1. Abdel-Basset M, Chang V, Mohamed R. “A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems.” 2021. [Google Scholar] [Crossref]

2. “Analysis of Biomedical Images Through Deep Learning Model for the Identification of Diseases.” 2025. [Google Scholar] [Crossref]

3. “Automated Analysis of Biomedical Images Using Convolutional Neural Networks and Deep Learning.” 2025. [Google Scholar] [Crossref]

4. J. Brown et al., “Automated segmentation of cardiac structures in CT angiography: Leveraging deep learning for precise diagnosis,” 2023. [Google Scholar] [Crossref]

5. G. Calzada-Jasso et al., “Generative Fabrication of Medical Images for Machine Learning Training,” 2025. [Google Scholar] [Crossref]

6. G. Carneiro, J. Nascimento, and A. Bradley, “Automated analysis of unregistered multi-view mammograms with deep learning,” 2017. [Google Scholar] [Crossref]

7. Chen A, Zhu L, Zang H, Ding Z, Zhan S. “Computer-aided diagnosis and decision-making system for medical data analysis: a case study on prostate MR images.” 2019. [Google Scholar] [Crossref]

8. P. Corcoran et al., “Chest X-Ray Visual Saliency Modeling: Eye-Tracking Dataset and Saliency Prediction Model,” 2025. [Google Scholar] [Crossref]

9. Das A, Rad P. “Opportunities and challenges in explainable artificial intelligence (XAI): A survey.” 2020. [Google Scholar] [Crossref]

10. Gadgil SU, Endo M, Wen E, Ng AY, Rajpurkar P. “Proceedings of the Fourth Conference on Medical Imaging with Deep Learning.” 2021. [Google Scholar] [Crossref]

11. G. Garcia et al., “Deep learning-based segmentation of liver lesions in ultrasound images,” 2023. [Google Scholar] [Crossref]

12. Ghnemat R, Alodibat S, Abu Al-Haija Q. “Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification.” 2023. [Google Scholar] [Crossref]

13. Guo K, Ren S, Bhuiyan MZA, Li T, Liu D, Liang Z, Chen X. “MDMaaS: Medical-Assisted Diagnosis Model as a Service with artificial intelligence and trust.” 2020. [Google Scholar] [Crossref]

14. Jafari M, Auer D, Karimi D. “DRU-Net: An efficient deep convolutional neural network for medical image segmentation.” 2020. [Google Scholar] [Crossref]

15. Janik A., Dodd J., Ifrim G., Sankaran K., Curran K. “Interpretability of a deep learning model in the application of cardiac mri segmentation with an acdc challenge dataset.” 2021. [Google Scholar] [Crossref]

16. Kazi A, Farghadani S, Aganj I, Navab N. “IA-GCN: Interpretable attention based graph convolutional network for disease prediction.” 2024. [Google Scholar] [Crossref]

17. Karimi D, Dou H, Warfield SK, Gholipour A. “Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis.” 2020. [Google Scholar] [Crossref]

18. Kayalibay B, Jensen G, van der Smagt P. “CNN-based segmentation of medical imaging data.” 2017. [Google Scholar] [Crossref]

19. Kebaili et al., “Deep learning approaches for data augmentation in medical imaging,” 2023. [Google Scholar] [Crossref]

20. Lekadir K, Osuala R, Gallin C, Lazrak N, Kushibar K, Tsakou G, et al. “FUTURE-AI: Guiding principles and consensus recommendations for trustworthy artificial intelligence in medical imaging.” 2023. [Google Scholar] [Crossref]

21. P. Li, Y. Pei, J. Li, and H. Xie, “Medical Image Recognition Using a Novel Neural Network Construction Controlled by a Kernel Method Encoder,” 2024. [Google Scholar] [Crossref]

22. Liu Q et al., “Imaging-Process-Informed Generative Strategy for Microwave Medical Image Processing,” 2025. [Google Scholar] [Crossref]

23. Liu X, Song L, Liu S, Zhang Y. “A review of deep-learning-based medical image segmentation methods.” 2021. [Google Scholar] [Crossref]

24. G. Litjens et al., “A survey on deep learning in medical image analysis,” 2017. [Google Scholar] [Crossref]

25. M. A. Mazurowski et al., “Deep learning in radiology: State of the art,” 2019. [Google Scholar] [Crossref]

26. Meng Y, Zhang H, Wang Z, Chen M, Hu P, Du Y, et al. “BI-GCN: Boundary-aware input-dependent graph convolution network for biomedical image segmentation.” 2021. [Google Scholar] [Crossref]

27. Miao J, Chen C, Liu F, Wei H, Heng PA. “CauSSL: Causality-inspired semi-supervised learning for medical image segmentation.” 2023. [Google Scholar] [Crossref]

28. E. Najdenovska et al., “Automated Identification of Eye Motion in Raw MRI Data Using Machine Learning,” 2025. [Google Scholar] [Crossref]

29. R. Rajendran and S. G. Shiva Shankar, “An overview of X-ray imaging in the medical field,” 2012. [Google Scholar] [Crossref]

30. Ruan J, Xiang S. “VM-UNet: Vision Mamba UNet for Medical Image Segmentation.” 2024. [Google Scholar] [Crossref]

31. Singh D, Somani A, Horsch A, Prasad DK. “Counterfactual explainable gastrointestinal and colonoscopy image segmentation.” 2022. [Google Scholar] [Crossref]

32. Smith and B. Johnson, “Revolutionizing X-ray imaging: CNN-based lung tissue segmentation in chest X-rays,” 2020. [Google Scholar] [Crossref]

33. Stoyanov D, Taylor Z, Kia SM, Oguz I, Reyes M, Martel A, Maier-Hein L, Marquand AF, Duchesnay E, Löfstedt T, Landman B, Cardoso MJ, Silva CA, Pereira S, Meier R. “Understanding and interpreting machine learning in medical image computing and applications.” 2018. [Google Scholar] [Crossref]

34. Z. Teng, L. Li, Z. Xin, D. Xiang, J. Huang, H. Zhou, F. Shi, W. Zhu, J. Cai, T. Peng, and X. Chen, “A literature review of artificial intelligence (AI) for medical image segmentation: From AI and explainable AI to trustworthy AI,” 2024. [Google Scholar] [Crossref]

35. Tragakis A, Kaul C, Murray-Smith R, Husmeier D. “The fully convolutional transformer for medical image segmentation.” 2023. [Google Scholar] [Crossref]

36. Williams D, Brown L. “Multimodal integration of clinical data, demographics, and imaging for enhanced disease classification in X-ray images.” 2021. [Google Scholar] [Crossref]

37. Yadav, N. L., Singh, S., Kumar, R., & Singh, S. “Medical image analysis for detection, treatment and planning of disease using artificial intelligence approaches.” 2024. [Google Scholar] [Crossref]

38. W. Zhang et al., “Annular Prior Prompt Learning for Medical Images Segmentation,” 2026. [Google Scholar] [Crossref]

39. M. A. Mazurowski et al., “Deep learning in radiology: State of the art,” 2019. [Google Scholar] [Crossref]