Proposal
Introduction
The outbreak of COVID-19 has been a global crisis, impacting most countries and territories around the world. Despite the social distancing and restrictive movement policies, cases have significantly grown during the past three years. During the beginning of the outbreak, the cause of and treatments for the corona virus were unclear. However, over time similar symptoms across various patients have occurred, such as shortness of breath, headaches, persistent pain or pressure in the chest, fever, nausea, and fatigue. More detailed observations of these symptoms with X-rays or CT scans show patchy and confluent opacity and consolidation distributed throughout the lung zone developed over time. Chest X-rays and CT scans can be a critical warning and useful diagnosis of COVID-19 and COVID-19 pneumonia, also used during in the follow-up procedure of recovery [5]. Thus, we believe a technological approach to such images can improve accuracy and speed of diagnosis and recovery, showing an end to the long tunnel of the pandemic.
Related Work
A number of works present works of analyzing chest X-rays to detect COVID-19, mostly through Deep Learning models such as Convolutional Neural Networks (CNN) [1, 3, 2, 7, 4]. Moreover, the effectiveness of different architectures within CNN model have been evaluated, such as AlexNet, VGG16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classifying COVID-19 cases from normal cases. In related work, hyperparameters have been tuned within these models including batch size, learning rate, number of epochs, and type of optimizers through a comparative analysis to achieve the best suited model. However, most of prior work has been focused on CNN models. In this project, we aim to investigate different possibilities of models including XGBoost classifier, multinomial logistic regression, anomaly detection and support vector machines. These approaches are often used for image classification, and we aim to understand why or why not different approaches are effective.
Methodology
We plan to use the dataset from Kaggle (https://www.kaggle.com/bachrr/covid-chest-xray) which includes COVID-19 cases with chest X-ray or CT images(see Figure a). It contains COVID-19 cases as well as MERS, SARS, and ARDS. Labels are arranged in hiearchy in Figure b.
(a) Chest X-ray of COVID
(b) Labels in Hierarchy
As we plan to use various models we might need to preprocess the images and resize them to be fed into the models. A technique called ”lung segmentation” has also been proposed in previous research [6] where it segments the lung and discards rest of the CXRs to prune away potential bias such as the presence of a medical device. While training we need to consider that the COVID datasets are built mainly of positive cases therefore the models will be trained with a k-fold cross-validation procedure which involves splitting the training dataset into k folds. However this might not be enough for handling imbalanced datasets so tuning parameters will be considered such as balancing the positive and negative weights during the training process.
Future Work
Evaluation
We will train our machine learning models by splitting the data in 67/33% for training/testing. Also,
we plan to use the k-fold cross validation approach to estimate the performance with less variance
than a single train-test split of the data. Based on the dataset size for CT and X-rays we will choose
the k value between 3, 5 and 10. Then we will compare the models performance with accuracy along
with Area Under the Curve (AUC), one of the most important evaluation metrics for checking any
classification model’s performance.
Timeline
References
[1] R. Jain, M. Gupta, S. Taneja, and D. J. Hemanth. Deep learning based detection and analysis of
covid-19 on chest x-ray images. Applied Intelligence, 51(3):1690–1700, 2021.
[2] A. I. Khan, J. L. Shah, and M. M. Bhat. Coronet: A deep neural network for detection and
diagnosis of covid-19 from chest x-ray images. Computer methods and programs in biomedicine,
196:105581, 2020.
[3] S. R. Nayak, D. R. Nayak, U. Sinha, V. Arora, and R. B. Pachori. Application of deep learning techniques for detection of covid-19 cases using chest x-ray images: A comprehensive study.
Biomedical Signal Processing and Control, 64:102365, 2021.
[4] C. Ouchicha, O. Ammor, and M. Meknassi. Cvdnet: A novel deep learning architecture for detection of coronavirus (covid-19) from chest x-ray images. Chaos, Solitons & Fractals, 140:110245,
2020.
[5] L. A. Rousan, E. Elobeid, M. Karrar, and Y. Khader. Chest x-ray findings and temporal lung
changes in patients with covid-19 pneumonia. BMC Pulmonary Medicine, 20(1):1–9, 2020.
[6] E. Tartaglione, C. A. Barbano, C. Berzovini, M. Calandri, and M. Grangetto. Unveiling covid-19 from chest x-ray with deep learning: A hurdles race with small data.
International Journal of Environmental Research and Public Health, 17(18):6933, Sep
2020. URL: http://dx.doi.org/10.3390/ijerph17186933, https://doi.org/10.3390/ijerph17186933
doi:10.3390/ijerph17186933.
[7] L. Wang, Z. Q. Lin, and A. Wong. Covid-net: A tailored deep convolutional neural network design
for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1):1–12, 2020