X-Ray and MRI Scan Analyzer with Automated Report Generation

Abstract

The integration of artificial intelligence (AI) into medical imaging is a paradigm shifting era in diagnostic healthcare. Conventional analysis of X-ray and MRI scans relies heavily on expert radiologists, a resource-intensive and time-consuming process prone to human error. This research introduces an AI-powered diagnostic assistant specifically designed to automate and augment the interpretation of X-ray and MRI images. Built on convolutional neural networks (CNNs) and advanced computer vision algorithms, the system can identify anomalies such as tumors, fractures, infections, and degenerative conditions with high accuracy. The backend is developed using Python and TensorFlow, with image preprocessing handled via OpenCV. A Django-powered web interface enables clinicians to upload images, receive automated diagnoses, and review annotated results in real time. The system has been evaluated on benchmark medical imaging datasets and achieves high precision, recall, and F1 scores across multiple pathology classes. It also incorporates explainable AI (XAI) features such as Grad-CAM visualizations to ensure interpretability for clinicians. The findings underscore the significant role of AI in supporting radiologists, reducing diagnostic turnaround times, and expanding access to quality healthcare.

Introduction

???? Overview

Artificial Intelligence (AI) is revolutionizing healthcare, especially in medical imaging, where it assists in interpreting X-rays and MRIs. This is crucial in areas lacking radiologists and helps reduce diagnostic delays and errors. Human interpretation is prone to fatigue, bias, and high workload—problems that AI can help mitigate.

???? Objective

The research presents an AI-based assistant using Convolutional Neural Networks (CNNs) to detect abnormalities like pneumonia, tumors, and fractures from X-ray and MRI scans. Key goals:

• Improve diagnostic accuracy

• Reduce turnaround time

• Support radiologists, not replace them

???? Key Features

• Dual-modality support: works with both X-ray and MRI

• Explainable AI (XAI) using Grad-CAM to visualize which parts of the image influence predictions

• Web-based interface (built with Django) for real-time diagnosis and clinician use

• Lightweight and modular, suitable for rural and resource-limited settings

• Open-source and extensible design

???? Related Work

• CheXNet and U-Net pioneered deep learning in radiology

• Commercial tools like Qure.ai and Aidoc focus on single modalities or need high-end infrastructure

• This project expands the scope to dual-modality, real-time analysis with full transparency

???? System Architecture

The system is modular and consists of:

1. Data Acquisition & Preprocessing

   • Uses NIH ChestX-ray14 and BraTS MRI datasets

   • Techniques: resizing, contrast enhancement, noise filtering (OpenCV)

2. CNN Model

   • Custom CNN architecture (ReLU, Softmax, Dropout, etc.)

   • Achieved 95.6% accuracy on X-rays, 93.8% on MRIs

   • Transfer learning supported (ResNet50, DenseNet121)

3. Explainability Layer

   • Integrates Grad-CAM to generate heatmaps for interpretability

4. Backend

   • Built with Python 3.12 and Django 5.x

   • Stores logs and metadata in PostgreSQL

   • Provides REST APIs for model interaction

5. Frontend

   • HTML/CSS/JS interface for image upload, results display, and PDF downloads

   • Designed for use on tablets/laptops in clinical environments

6. Security

   • CSRF protection, HTTPS, file validation

???? Technologies Used

???? Performance Results

Grad-CAM visualizations increase clinical trust, enabling users to interpret AI decisions more confidently.

Conclusion

The X-Ray and MRI Scan Analyzer demonstrates the effective use of AI in automating medical image interpretation. By integrating convolutional neural networks and explainable AI (Grad-CAM), the system delivers accurate, fast, and interpretable diagnostic results for X-ray and MRI images. Built with a modular architecture using Django and TensorFlow, the solution supports real-time image uploads, prediction, and visual explanation through a web interface. The system reduces the diagnostic burden on radiologists and is especially useful in settings where access to specialists is limited. It is intended not to replace clinicians but to support them by offering a reliable second opinion and improving turnaround times for image-based diagnoses. Importantly, the xray system is not designed to replace medical professionals or doctors but to assist them. As a decision support tool, it augments clinical judgment, reduces time-to-diagnosis, and potentially improves patient outcomes, particularly where access to specialists is limited.

References

[1] Rajpurkar, P., et al. (2017). \"CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning.\" arXiv:1711.05225. [2] Isensee, F., et al. (2021). \"nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.\" Nature Methods. [3] Selvaraju, R. R., et al. (2017). \"Grad-CAM: Visual Explanations from Deep Networks.\" ICCV. [4] Litjens, G., et al. (2017). \"A Survey on Deep Learning in Medical Image Analysis.\" Medical Image Analysis. [5] Simonyan, K., & Zisserman, A. (2014). \"Very Deep Convolutional Networks for Large-Scale Image Recognition.\" arXiv. [6] Chollet, F. (2015). \"Keras: Deep Learning Framework.\" [7] Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., ... & Zhang, K. (2018). Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, 172(5), 1122–1131. [8] Bradski, G. (2000). \"The OpenCV Library.\" Dr. Dobb’s Journal of Software Tools. [9] Django Project. (2024). \"Django Documentation 5.x.\" https://docs.djangoproject.com/ [10] American Heart Association. (2023). Cardiomegaly (Enlarged Heart): Causes, Symptoms and Treatment. Retrieved from https://www.heart.org

Copyright

Copyright © 2025 Katta Thanuj Santhosh Kumar, K.S.S. Soujanya Kumari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.