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+# Parallelized Deep Convolutional Neural Networks for Pathology Detection and Localization in Chest X-Rays
+
+_This repository contains the code for the prototype application I developed for the Final Research Project of the
+**BSc. (Hons.) in Computer Science** degree at University of Westminster (taught at IIT Sri Lanka)_
+
+## Authors
+
+- [Ravidu Silva](mailto:ravidus.ac@gmail.com)
+- [Pumudu Fernando (Supervisor)](mailto:pumudu.f@iit.ac.lk)
+
+## Publication
+Effective Utilization of Multiple Convolutional Neural Networks for Chest X-Ray Classification - Springer Nature
+- [https://doi.org/10.1007/s42979-022-01390-9](https://doi.org/10.1007/s42979-022-01390-9)
+
+## Project Introduction
+
+Radiography is a prevalent method of medical diagnosis, especially in humans. Out of the various types of Radiography,
+Chest Radiography holds an important place due to the numerous diseases diagnosed through it. These diseases vary from
+low-risk diseases to high-risk, life-threatening diseases. Due to this, accurate diagnosis of Chest X-Rays is considered
+very crucial. This research project presents a novel way of utilizing multiple Convolutional Neural Networks for
+accurate detection and localization of diseases present in Chest X-Ray images. The proposed algorithm creates a range of
+new pathways to conduct research in a variety of fields and use cases. The research also aims to prove the proposed
+algorithm's strengths and advantages for Chest X-Ray classification within a well-defined scope.
+
+## Project Pitch
+
+[![PROJECT PITCH](https://img.youtube.com/vi/0PmXOY-Mt1k/0.jpg)](https://www.youtube.com/watch?v=0PmXOY-Mt1k)
+
+## Full Project Demonstration
+
+[![PROJECT DEMO](https://img.youtube.com/vi/SBVE1NVDHcA/0.jpg)](https://www.youtube.com/watch?v=SBVE1NVDHcA)
+
+## Technologies
+
+Following are the main technologies used in this project
+
+- Python
+- TensorFlow, Keras
+- Numpy
+- Flask
+- HTML5, CSS, JS
+
+## Application Installation
+
+### 1. Pre-requisites
+
+- Python 3.7+
+- PIP
+- CUDA supported GPU with at least 10GB VRAM
+  - CUDA installation
+
+### 2. Install Dependencies
+
+   ```
+   pip install -r requirements.txt
+   ```
+
+### 3. Model file placement
+
+- Place the model files in their respective folders
+
+### 4. Running the Application
+
+```
+python3 app.py --host=0.0.0.0 --port=5000 --cert=adhoc --no-reload
+```
+
+After the execution of this line you can visit you localhost to use the application
+
+## Model Accuracy
+
+### Models Names
+
+- R-50v2: ResNet50v2
+- D-121: DenseNet-121
+- D-169: DenseNet-169
+- R-D-Ens: Ensemble of ResNet50v2, DenseNet-121 and DenseNet-169
+- P-64: ParallelXNet (ratio: 64)
+- P-128: ParallelXNet (ratio: 128)
+- P-Ens: Ensemble of P-64 and P-128
+
+### Test results on MIMIC-CXR 2020
+
+|Pathology \Model|R-50v2|D-121|D-169|R-D-Ens|P-64|P-128|P-Ens|
+|:--------------:|:------:| :-----:| :-----:| :-----:| :-----:| :-----:| :-----:|
+|Enlarged Cardiom.|0.7026|0.7048|**0.7209**|0.7159|0.7061|0.7076|0.7107|
+|Cardiomegaly|0.7808|0.7807|0.7888|0.7889|0.7921|0.7874|**0.7932**|
+|Lung Lesion|0.6965|0.7053|0.7111|0.7109|0.7155|0.7157|**0.7192**|
+|Lung Opacity|0.6899|0.6946|0.6967|0.7000|0.6978|0.7007|**0.7031**|
+|Edema|0.8357|0.8389|**0.8434**|0.8432|0.8403|0.8391|0.8419|
+|Consolidation|0.7475|0.7507|0.7548|0.7580|**0.7605**|0.7514|0.7597|
+|Pneumonia|0.7116|0.7228|0.7289|0.7302|0.7341|0.7303|**0.7372**|
+|Atelectasis|0.7634|0.7627|0.7668|0.7688|0.7674|0.7680|**0.7703**|
+|Pneumothorax|0.8467|0.8691|0.8640|0.8690|0.8595|**0.8711**|0.8706|
+|Pleural Effusion|0.8897|0.8921|0.8941|0.8957|0.8971|0.8952|**0.8985**|
+|Pleural Other|0.8067|0.8255|**0.8544**|0.8396|0.8313|0.8504|0.8466|
+|Fracture|0.6613|**0.6944**|0.6894|0.6891|0.6933|0.6810|0.6916|
+|Support Devices|0.8661|0.8994|0.9029|0.9041|0.9039|0.9070|**0.9085**|
+
+- ‘ParallelXNet’ is better at **9 out of 13** labels of the dataset.
+
+### Test results on ChestX-ray-14
+
+|Pathology \Model|R-50v2  |D-121   |D-169   |R-D-Ens |P-64    |P-128   |P-Ens   |
+|:--------------:|:------:| :-----:| :-----:| :-----:| :-----:| :-----:| :-----:|
+|Nodule|0.7585|0.7736|0.7762|0.7817|0.7826|0.7807|**0.7875**|
+|Cardiomegaly|0.8770|0.8876|0.8873|0.8943|0.8901|0.8927|**0.8958**|
+|Emphysema|0.9098|0.9276|0.9259|0.9288|0.9294|0.9312|**0.9335**|
+|Fibrosis|0.8183|0.8257|0.8359|0.8355|0.8321|0.8344|**0.8381**|
+|Edema|0.8397|0.8489|0.8471|0.8522|0.8502|0.8474|**0.8526**|
+|Consolidation|0.7389|0.7443|0.7505|0.7531|0.7529|0.7527|**0.7576**|
+|Pneumonia|0.7137|0.7287|0.7351|0.7337|0.7386|0.7353|**0.7411**|
+|Atelectasis|0.7741|0.7799|0.7807|0.7868|0.7863|0.7823|**0.7888**|
+|Pneumothorax|0.8649|0.8730|0.8733|**0.8787**|0.8720|0.8740|0.8773|
+|Effusion|0.8248|0.8338|0.8343|0.8376|0.8359|0.8370|**0.8399**|
+|Mass|0.8128|0.8342|0.8259|0.8361|0.8329|0.8414|**0.8433**|
+|Infiltration|0.6895|0.7013|0.7031|**0.7047**|0.6984|0.7028|0.7041|
+|Hernia|0.8703|0.8767|0.8847|0.8880|0.8742|0.8905|**0.8911**|
+|Pleural Thickening|0.7742|0.7899|0.7918|**0.7949**|0.7897|0.7889|0.7942|
+
+- ‘ParallelXNet’ is better at **11 out of 14** labels of the dataset.
+
+### Additional testing on CIFAR-10
+
+To further confirm the abilities of ParallelXNet, it was tested on a non-medical dataset.
+
+| Model Metric | R-D-Ens | P-Ens |
+| --- | :---: | :---: |
+| Sensitivity | 87.94% | **88.55%** |
+| Specificity | 98.66% | **98.75%** |
+| Precision | 88.00% | **88.57%** |
+| Accuracy | 97.58% | **97.72%** |
+| Balanced Accuracy | 93.30% | **93.64%** |
+| F1-Score | 87.94% | **88.56%** |
+| MCC | 0.8663 | **0.8729** |
+
+- ParallelXNet is better in terms of **all the metrics** considered for CIFAR-10 dataset.
+
+## Acknowledgements
+
+We acknowledge below experts for the contribution of their valuable knowledge throughout this project
+
+- Dr. Nilmini Fernando (MBBS, DFM)
+- Dr. Harshana Bandara (MBBS, MD)
+- Dr. Prasantha De Silva (MBBS, MSc)