Dr. Andrew Hamilton-Wright Profile

Dr. Andrew Hamilton-Wright

at Univ of Guelph

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Publications (2)

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SPIE Journal Paper | 23 August 2021 Open Access

Detection of COVID-19 from chest x-ray images using transfer learning

Jenita Manokaran, Fatemeh Zabihollahy, Andrew Hamilton-Wright, Eranga Ukwatta

JMI, Vol. 8, Issue S1, 017503, (August 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.S1.017503

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Purpose: The objective of this study is to develop and evaluate a fully automated, deep learning-based method for detection of COVID-19 infection from chest x-ray images. Approach: The proposed model was developed by replacing the final classifier layer in DenseNet201 with a new network consisting of global averaging layer, batch normalization layer, a dense layer with ReLU activation, and a final classification layer. Then, we performed an end-to-end training using the initial pretrained weights on all the layers. Our model was trained using a total of 8644 images with 4000 images each in normal and pneumonia cases and 644 in COVID-19 cases representing a large real dataset. The proposed method was evaluated based on accuracy, sensitivity, specificity, ROC curve, and F1-score using a test dataset comprising 1729 images (129 COVID-19, 800 normal, and 800 pneumonia). As a benchmark, we also compared the results of our method with those of seven state-of-the-art pretrained models and with a lightweight CNN architecture designed from scratch. Results: The proposed model based on DenseNet201 was able to achieve an accuracy of 94% in detecting COVID-19 and an overall accuracy of 92.19%. The model was able to achieve an AUC of 0.99 for COVID-19, 0.97 for normal, and 0.97 for pneumonia. The model was able to outperform alternative models in terms of overall accuracy, sensitivity, and specificity. Conclusions: Our proposed automated diagnostic model yielded an accuracy of 94% in the initial screening of COVID-19 patients and an overall accuracy of 92.19% using chest x-ray images.

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Proceedings Article | 15 February 2021 Presentation + Paper

Deep learning-based detection of COVID-19 from chest x-ray images

Jenita Manokaran, Fatemeh Zabihollahy, Andrew Hamilton-Wright, Eran Ukwatta

Proceedings Volume 11600, 116000O (2021) https://doi.org/10.1117/12.2581314

KEYWORDS: Chest imaging, Data modeling, Polymers, Image classification, Diagnostics

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COVID-19 is a highly contagious infectious disease that has infected millions of people worldwide. Polymerase Chain Reaction (PCR) is the gold standard diagnostic test available for COVID-19 detection. Alternatively, medical imaging techniques, including chest X-ray (CXR), has been instrumental in diagnosis and prognosis of patients with COVID-19. Enabling the CXR with machine learning-based automated diagnosis will be important for rapid diagnosis of the disease by minimizing manual assessment of images by the radiologists. In this work, we developed a deep learning model that utilizes the transfer learning approach using a pre-trained Residual Network model. The Residual Network 50 (ResNet50) is trained from scratch by utilizing the initial architecture and pre-trained weights to provide the classification results. Two types of classification (two-class and three-class) is performed using the developed model. A cascaded approach is adopted for two-class classification where the classification is performed in two phases. The dataset used for training and evaluating the model comprises of 8,254 images in total out of which 1651 images were considered for testing the cascaded model (15 COVID-19) and three-class classification (51 COVID-19). The model was evaluated using accuracy, sensitivity, specificity, and F1-score metrics. Our cascaded model yielded an accuracy of 91.8% for classification of abnormal and normal cases and 97.9% for the classification of pneumonia and COVID-19 images. In the three-class classification, our model reported an accuracy of 92% in classifying normal, pneumonia (bacterial and viral) and COVID-19 cases.

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