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 | AI-Driven Multi-Disease Detection and Severity Analysis from Chest X-ray Image |
|---|---|
| ABSTRACT | AI-Driven Multi-Disease Detection and Severity Analysis from Chest X-ray Images is an advanced healthcare an artificial intelligence-based solution to assist in the early diagnosis and evaluation of multiple diseases. In an introductory session to this system, participants will learn how AI models can analyze chest X-ray images to detect conditions such as pneumonia, tuberculosis, and other lung-related diseases with high accuracy. The webinar explains how deep learning methods, particularly convolutional neural networks (CNNs), can be used to uncover significant patterns from medical images. Through practical demonstrations, attendees will understand how raw X-ray images are pre-processed, analyzed, and classified into different disease categories. The system also focuses on severity analysis, where it evaluates the extent or stage of a detected disease, helping medical professionals make informed treatment decisions. Participants will learn more about how datasets are used to train models and how predictions are generated in real-time scenarios. Students will get fundamental understanding by the end of the program of building AI-based medical imaging systems, including model training, evaluation, and deployment. This project highlights the potential of AI in improving diagnostic efficiency, reducing human error, and supporting healthcare professionals in delivering faster and more accurate patient care. Regardless of your level of experience or someone interested in healthcare technology, this introduction provides essential understanding and hands-on exposure to AI-driven disease detection systems, opening new possibilities in medical innovation and data-driven healthcare solutions. |
| AUTHOR | P SHIRISHA, MAHESHWARI M DESAI PG Student, Dept. of MCA, City Engineering College, Bengaluru, India Assistant Professor, Dept. of MCA, City Engineering College, Bengaluru, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405052 |
| pdf/52_AI-Driven Multi-Disease Detection and Severity Analysis from Chest X-ray Image.pdf | |
| KEYWORDS | |
| References | 1. NIH Chest X-ray Dataset – Wang et al., “ChestX-ray8: Hospital-scale Chest X-ray Database,” 2017. 2. CheXpert Dataset – Irvin et al., Stanford ML Group, 2019. 3. MIMIC-CXR Dataset – Johnson et al., PhysioNet, 2019. 4. ResNet-50 – Kaiming He et al., “Deep Residual Learning for Image Recognition,” 2016. 5. DenseNet-121 – Gao Huang et al., “Densely Connected Convolutional Networks,” 2017. 6. VGG-16 – Karen Simonyan & Andrew Zisserman, 2014. 7. TensorFlow – Google, official documentation. 8. PyTorch – Meta AI Research, official documentation. 9. Flask – Pallets Project documentation. 10. Django – Django Software Foundation documentation. |
