&

Database

• The extended version of the COV19-CT-DB database was used. The project includes working on a chest CT-scan series of images to develop an automated algorithmic solution for COVID-19 detection, via ECCV 2022 Workshop: AI-enabled Medical Image Analysis – Digital Pathology & Radiology/COVID19 - 2nd COV19D Competition. The team (IDU-CVLab) is on the leaderboard here.
• The full methodology with image processing techniques was retrained and tested on the extended version of the COV19-CT Database.

Dependencies

• Numpy == 1.19.5
• CV2 == 4.5.4
• Tensorflow == 2.5.0

Methodology

This method can be applied in two different ways; With images processing and without (using the two different codes in the repository):

With images processing (Optional). Uppermost and lower most slcies of each CT scans were removed with 60% of the slices in each CT scan is kept. Next, Slices of the CT scan were manually cropped to better localize the Region of Interste (ROI), which is the two lung areas. For more theoratical details please refere to the second journal papers cited below.

Trnasfer Learning-Based Classifier. To take the diagnostic desicions at slices level, a transfer learning model (Xception model) with a modified output was deployed.

• To replicate the code properly, you would need a training set of images and a validation set of images. For more theoratical details please refere to the first journal papers cited below.
• The images must be put in the appropriate directories. With that, the directory of training and validation sets in the codes should be changed to match your directories. This method is following the documentation for ‘imagedatagenerator’ and ‘flow_from_directory’ at https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator
• Please note: this is a binary classification task. To replicate the method on multiple classes, you need to modify the model's output layer to suit your task.
• For full details, please check the attached papers.

Checking Model's Robustness against Noisey Images. Gussian noise was added to the images in the original validation set and then images are processed as mentioned above and the pretrained and saved model was tested on the newly-created noisey images. This step aims to check our method's performance against noisey data to prove the solution's robustness. Salt-and-paper was also added to the images to further validate the results with different types of noise. The python code is named "Noisey-Images-Image-Processing-Transfer-Learning.py".

Cite

If you find these methods useful, kindly consider citing the follwing two articles:

First journal paper
@article{IJAIN1432|to_array:0,
author = {Kenan Morani and Esra Kaya Ayana and Devrim Unay},
title = {Covid-19 detection using modified xception transfer learning approach from computed tomography images},
journal = {International Journal of Advances in Intelligent Informatics},
volume = {9},
number = {3},
year = {2023},
issn = {2548-3161}, pages = {524--536}, doi = {10.26555/ijain.v9i3.1432},
url = {http://ijain.org/index.php/IJAIN/article/view/1432}

AND

Second journal paper
author = {Kenan Morani, Esra Kaya Ayana, Dimitrios Kollias, Devrim Unay}
title = {COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier},
journal = {International Journal of Biomedical Imaging}
volume = {2024}
article ID = {9962839}
pages = {9 pages}
publisher = {Wiley}
year = {2024}
url = {https://doi.org/10.1155/2024/9962839}