Targeted Self Supervision for Classification on a Small COVID-19 CT Scan Dataset

Ewen, Nicolas; Khan, Naimul

doi:10.48550/arxiv.2011.10188

Cited by 1 publication

(1 citation statement)

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“…In addition, the accuracy, F 1 score and AUC achieved by the Resnet50 model integrated with the proposed method are respectively 16.22%, 12.71%, and 12.12% higher than the ResNet50 used alone [14]. In general, although recall and AUC are slightly lower than the VGG19 model results [41] and the DenseNet169 model [42], our enhanced ResNet50 model outperforms the other models in Table II in terms of accuracy, precision and F 1 score.…”

Section: Experiments On Covid19-ct Datasetmentioning

confidence: 80%

Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis

Xu¹,

Wang²,

Sowmya³

2022

Preprint

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Coronavirus Disease 2019 (COVID-19) has spread globally and become a health crisis faced by humanity since first reported. Radiology imaging technologies such as computer tomography (CT) and chest X-ray imaging (CXR) are effective tools for diagnosing COVID-19. However, in CT and CXR images, the infected area occupies only a small part of the image. Some common deep learning methods that integrate large-scale receptive fields may cause the loss of image detail, resulting in the omission of the region of interest (ROI) in COVID-19 images and are therefore not suitable for further processing. To this end, we propose a deep spatial pyramid pooling (D-SPP) module to integrate contextual information over different resolutions, aiming to extract information under different scales of COVID-19 images effectively. Besides, we propose a COVID-19 infection detection (CID) module to draw attention to the lesion area and remove interference from irrelevant information. Extensive experiments on four CT and CXR datasets have shown that our method produces higher accuracy of detecting COVID-19 lesions in CT and CXR images. It can be used as a computer-aided diagnosis tool to help doctors effectively diagnose and screen for COVID-19.

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Section: Experiments On Covid19-ct Datasetmentioning

confidence: 80%