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
• Pumudu Fernando (Supervisor)

Publication

Effective Utilization of Multiple Convolutional Neural Networks for Chest X-Ray Classification - Springer Nature

• 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

Full Project Demonstration

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)