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+#  Panoptic Image Segmentation for Lung Disease Detection
+This project was developed by students of VIT Bhopal as part of Project Exhibition 2 at VIT Bhopal University, under the Department of Computer Science and Engineering.
+
+This project uses deep learning (U-Net) to segment lungs and detect lung diseases from X-ray images. It includes a web-based interface where users can upload an X-ray, view segmentation results, and get a downloadable PDF report with predictions.
+
+
+## Project Objective
+To develop an intelligent web-based platform that leverages deep learning (U-Net architecture) for panoptic segmentation and classification of lung diseases (COVID-19, Pneumonia, Lung Opacity) from chest X-ray images. The system provides:
+
+Real-time segmentation output
+Disease prediction with severity estimation
+AI-generated doctor-style comments
+A downloadable PDF report 
+
+## Purpose
+The objective of this project is to leverage deep learning for early and accurate detection of lung diseases like COVID-19, Pneumonia, and Lung Opacity through X-ray images using panoptic segmentation techniques. The project combines medical AI, web application development, and report automation to provide a complete diagnostic support system.
+
+## Formats
+    - All the images are in Portable Network Graphics (PNG) file format and resolution are 299*299 pixels.
+
+## Features
+
+- Upload Chest X-ray images (via UI)
+- Automatic lung & disease-affected region segmentation using U-Net
+- Disease classification and severity estimation
+- PDF Report generation (with image, result, and patient info)
+- Email report to the patient (optional)
+- Manual segmentation feature (optional)
+- Full Flask + HTML + JS (AJAX) stack
+
+
+## Model Info
+Architecture: U-Net
+
+Input: Chest X-ray images (typically 256x256 or 512x512)
+Output: Segmentation mask (disease regions)
+Dataset Used: COVID-19 Radiography Dataset from Kaggle
+
+## Future Improvements
+Panoptic segmentation with instance + semantic labeling
+More advanced disease classification (multi-label)
+Responsive frontend dashboard
+Live segmentation feedback
+
+## Credits
+U-Net architecture: Olaf Ronneberger et al.
+COVID dataset: Kaggle contributors
+PDF & Email: ReportLab, pdfkit, smtplib
+
+## TEAM MEMBER 
+1. PREETAM VERMA 22MIM10115 (LEAD THE PROJECT)
+2. VIPIN VERMA 22MIM10118
+3. VISHAL VERMA 22MIM10126
+4. AYUSH VERMA 22MIM10123
+5. DEVENDRA VERMA 22MIM10108
+
+## Team Members & Contribution
+
+## Name	        Roll          Number	          Contribution
+Preetam Verma	22MIM10115	  Team Lead,          Model Integration, Backend & Deployment, Testing & Debugging
+Vipin Verma	    22MIM10118	  Data Preprocessing, Model Training
+Vishal Verma	22MIM10126	  UI/UX Design,       Frontend Development
+Ayush Verma	    22MIM10123	  Data Preprocessing, Model Training
+Devendra Verma	22MIM10108	  Report Generation,  Testing & Debugging
+
+## Faculty Guidance
+The project was carried out under the guidance of 
+Supervisor: Mrs. Garima Jain
+Reviewers: Dr. Harshlata Vishwakarma, Dr. Priscilla Dinkar Moyya
+
+
+
+##  ABOUT dataset 
+
+COVID-19 CHEST X-RAY DATABASE
+
+A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors have created a database of chest X-ray images for COVID-19 positive cases along with Normal and Viral Pneumonia images. This COVID-19, normal and other lung infection dataset is released in stages. In the first release we have released 219 COVID-19, 1341 normal and 1345 viral pneumonia chest X-ray (CXR) images. In the first update, we have increased the COVID-19 class to 1200 CXR images. In the 2nd update, we have increased the database to 3616 COVID-19 positive cases along with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection) and 1345 Viral Pneumonia images and corresponding lung masks. We will continue to update this database as soon as we have new x-ray images for COVID-19 pneumonia patients.  
+
+
+COVID-19 data:
+-----------------------
+COVID data are collected from different publicly accessible dataset, online sources and published papers.
+-2473 CXR images are collected from padchest dataset[1].
+-183 CXR images from a Germany medical school[2].
+-559 CXR image from SIRM, Github, Kaggle & Tweeter[3,4,5,6]
+-400 CXR images from another Github source[7].
+
+
+Normal images:
+---------------------------------------- 
+10192 Normal data are collected from from three different dataset.
+-8851 RSNA [8]
+-1341 Kaggle [9]
+
+
+Lung opacity images:
+---------------------------------------- 
+6012 Lung opacity CXR images are collected from Radiological Society of North America (RSNA) CXR dataset  [8]
+
+Viral Pneumonia images:
+---------------------------------------- 
+1345 Viral Pneumonia data are collected from  the Chest X-Ray Images (pneumonia) database [9]
+
+Please cite the follwoing two articles if you are using this dataset:
+-M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676.
+-Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images. arXiv preprint arXiv:2012.02238.
+
+Reference:
+[1]https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711
+[2]https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png
+[3]https://sirm.org/category/senza-categoria/covid-19/
+[4]https://eurorad.org
+[5]https://github.com/ieee8023/covid-chestxray-dataset
+[6]https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328
+[7]https://github.com/armiro/COVID-CXNet  
+[8]https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data
+[9] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
+