COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network

• Autorzy: Shibu George Gerosh , Raj Mishra Pratyush , Sinha Panav , Ranjan Prusty Manas

Identyfikatory

DOI

10.1016/j.bbe.2022.11.003

• Języki publikacji: EN

Abstrakty

EN

COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Most of existing methods used for this application involve the usage of pretrained models such as VGG19, ResNet, DenseNet, Xception, and EfficeintNet which were trained on RGB image datasets. X-rays are fundamentally single channel images, hence using RGB trained model is not appropriate since it increases the operations by involving three channels instead of one. A way of using pretrained model for grayscale images is by replicating the one channel image data to three channel which introduces redundancy and another way is by altering the input layer of pretrained model to take in one channel image data, which comprises the weights in the forward layers that were trained on three channel images which weakens the use of pre-trained weights in a transfer learning approach. A novel approach for identification of COVID-19 using CXRs, Contrast Limited Adaptive Histogram Equalization (CLAHE) along with Homomorphic Transformation Filter which is used to process the pixel data in images and extract features from the CXRs is suggested in this paper. These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. Evaluation of the suggested model is done with the help of two publicly available datasets; one to obtain COVID-19 and No-Finding images and the other to obtain Pneumonia CXRs. The dataset comprises 6750 images in total; 2250 images for each class. Results obtained show that the model has achieved 96.56% for multi-class classification and 98.06% accuracy for binary classification using 5-fold stratified cross validation (CV) method. This result is competitive and up to the mark when compared with the performance shown by existing approaches for COVID-19 classification.

Słowa kluczowe

EN

COVID-19 detection; homomorphic transformation filter; deep CNN; convolutional neural network; VGG; ANOVA

PL

wykrywanie COVID-19; sieć neuronowa konwolucyjna; VGG; ANOVA

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 1
• Strony: 1--16
• Opis fizyczny: Bibliogr. 64 poz., rys., tab., wykr.

Twórcy

autor

Shibu George Gerosh

• School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

autor

Raj Mishra Pratyush

• School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

autor

Sinha Panav

• School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

autor

Ranjan Prusty Manas

• Centre for Cyber Physical Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)