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 | Lung Disease Prediction using Data Science with Patient Care Recommendations & Suggestions |
|---|---|
| ABSTRACT | This paper presents Lungs disease prediction, a web-based lung disease prediction system designed to support clinicians and patients in the early identification and management of pulmonary conditions. Respiratory diseases including Pneumonia, Tuberculosis, COVID-19, Pleural Effusion, and Normal conditions collectively contribute to significant global mortality. The proposed system provides an end-to-end diagnostic pipeline through a chest X-ray image upload interface, where the uploaded image is analyzed using a deep learning model to classify the lung condition and predict its severity. The model is based on a DenseNet201 Convolutional Neural Network (CNN) architecture trained using transfer learning, which classifies chest radiographs into five categories: COVID-19, Pleural Effusion, Normal, Pneumonia, and Tuberculosis. The application includes additional features such as a symptom checker, AI chatbot assistant, breathing exercise module, dietary guidance, and a wellness tracker to encourage proactive health management. A doctor dashboard is integrated into the system, allowing medical professionals to monitor patient scan histories and review predicted risk levels. The system is implemented using Flask for the backend, HTML/CSS/JavaScript for the frontend, SQLite (Lungs disease prediction.db) for data storage, and TensorFlow/Keras for deep learning inference. The overall framework aims to provide early detection, real-time prediction, and patient-centred guidance for lung disease management. |
| AUTHOR | PROF. VISHAKHA JADHAV, ASHUTOSH MUJMULE, SWARAJ CHAVHAN, DNYANESHWARI SHINDE, SHRUTIKA WALUNJ Guide, Computer Engineering Department of Computer Engineering, Dhole Patil College of Engineering, Pune, Maharashtra, India Final Year B.E. Student, Department of Computer Engineering, Dhole Patil College of Engineering, Pune, Maharashtra, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406044 |
| pdf/44_Lung Disease Prediction using Data Science with Patient Care Recommendations & Suggestions.pdf | |
| KEYWORDS | |
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