Keyword search (4,163 papers available)

"Heidarian S" Authored Publications:

Title Authors PubMed ID
1 Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module Khademi S; Heidarian S; Afshar P; Mohammadi A; Sidiqi A; Nguyen ET; Ganeshan B; Oikonomou A; 41150036
ENCS
2 Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans Khademi S; Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Oikonomou A; Shafiee A; Babaki Fard F; Plataniotis KN; Mohammadi A; 36862633
ENCS
3 Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network Afshar P; Rafiee MJ; Naderkhani F; Heidarian S; Enshaei N; Oikonomou A; Babaki Fard F; Anconina R; Farahani K; Plataniotis KN; Mohammadi A; 35318368
ENCS
4 COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images Enshaei N; Oikonomou A; Rafiee MJ; Afshar P; Heidarian S; Mohammadi A; Plataniotis KN; Naderkhani F; 35217712
ENCS
5 COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Babaki Fard F; Samimi K; Atashzar SF; Oikonomou A; Plataniotis KN; Mohammadi A; 34113843
ENCS
6 COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images. Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A 32958971
ENCS

 

Title:COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images.
Authors:Afshar P,  Heidarian S,  Naderkhani F,  Oikonomou A,  Plataniotis KN,  Mohammadi A
Link:https://www.ncbi.nlm.nih.gov/pubmed/32958971
DOI:10.1016/j.patrec.2020.09.010
Publication:Pattern recognition letters
Keywords:COVID-19 pandemic;  Capsule network;  Deep learning;  X-ray images; 
PMID:32958971 Category:Pattern Recognit Lett Date Added:2020-09-23
Dept Affiliation: ENCS
1 Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada.
2 Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada.
3 Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Canada.
4 Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.

Description:

COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images.

Pattern Recognit Lett. 2020 Sep 16; :

Authors: Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A

Abstract

Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To potentially and further improve diagnosis capabilities of the COVID-CAPS, pre-training and transfer learning are utilized based on a new dataset constructed from an external dataset of X-ray images. This is in contrary to existing works on COVID-19 detection where pre-training is performed based on natural images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.

PMID: 32958971 [PubMed - as supplied by publisher]




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