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COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing.
Hasan, Md Kamrul; Jawad, Md Tasnim; Hasan, Kazi Nasim Imtiaz; Partha, Sajal Basak; Masba, Md Masum Al; Saha, Shumit; Moni, Mohammad Ali.
  • Hasan MK; Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh.
  • Jawad MT; Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh.
  • Hasan KNI; Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh.
  • Partha SB; Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh.
  • Masba MMA; Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh.
  • Saha S; Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh.
  • Moni MA; Department of Computer Science & Engineering, Pabna University of Science and Technology, Pabna-6600, Bangladesh.
Inform Med Unlocked ; 26: 100709, 2021.
Article in English | MEDLINE | ID: covidwho-1373080
ABSTRACT
The novel COVID-19 is a global pandemic disease overgrowing worldwide. Computer-aided screening tools with greater sensitivity are imperative for disease diagnosis and prognosis as early as possible. It also can be a helpful tool in triage for testing and clinical supervision of COVID-19 patients. However, designing such an automated tool from non-invasive radiographic images is challenging as many manually annotated datasets are not publicly available yet, which is the essential core requirement of supervised learning schemes. This article proposes a 3D Convolutional Neural Network (CNN)-based classification approach considering both the inter-and intra-slice spatial voxel information. The proposed system is trained end-to-end on the 3D patches from the whole volumetric Computed Tomography (CT) images to enlarge the number of training samples, performing the ablation studies on patch size determination. We integrate progressive resizing, segmentation, augmentations, and class-rebalancing into our 3D network. The segmentation is a critical prerequisite step for COVID-19 diagnosis enabling the classifier to learn prominent lung features while excluding the outer lung regions of the CT scans. We evaluate all the extensive experiments on a publicly available dataset named MosMed, having binary- and multi-class chest CT image partitions. Our experimental results are very encouraging, yielding areas under the Receiver Operating Characteristics (ROC) curve of 0 . 914 ± 0 . 049 and 0 . 893 ± 0 . 035 for the binary- and multi-class tasks, respectively, applying 5-fold cross-validations. Our method's promising results delegate it as a favorable aiding tool for clinical practitioners and radiologists to assess COVID-19.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Inform Med Unlocked Year: 2021 Document Type: Article Affiliation country: J.imu.2021.100709

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Inform Med Unlocked Year: 2021 Document Type: Article Affiliation country: J.imu.2021.100709