A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

Ahamed, Khabir Uddin; Islam, Manowarul; Uddin, Md. Ashraf; Akhter, Arnisha; Paul, Bikash Kumar; Yousuf, Mohammad Abu; Uddin, Shahadat; Quinn, Julian M.W.; Moni, Mohammad Ali

doi:10.1016/j.compbiomed.2021.105014

Cited by 92 publications

(49 citation statements)

References 58 publications

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“…Table 13 shows quantitative comparison results including the metrics of accuracy and parameters in its network architecture. In fact, the accuracy from our proposed LightEfficientNetV2 model was slightly inferior or close to some of the studies [ 5 , 41 , 42 , 46 , 66 ]. However, in terms of the total number of parameters used in the model, the model proposed in this study is less than those in most other studies.…”

Section: Discussionsupporting

confidence: 52%

“… Moghaddam et al (2021) [ 46 ] 4001 COVID-19, 5705 non-informative, 9979 normal CT images. WCNN4 3 99.03% 4,610,531 Ahamed et al (2021) [ 66 ] 1143 covid-19, 1150 bacterial pneumonia, 1150 viral pneumonia, 1150 normal X-ray images. Modified & Tuned ResNet50V2 4 96.45% 49,210,756 1000 covid-19, 1000 normal, 1000 CAP CT images.…”

Section: Discussionmentioning

confidence: 99%

“…Compared with traditional machine learning methods, CNN have demonstrated great ability on COVID-19 detection by using chest X-ray or CT images jointly or separately. Most of the aforementioned development of the related studies proposed new networks aiming to obtain higher classification accuracy may not consider the number of parameters used in the network architecture or the number of images used in the training phase [ [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] ]. The objective of this study is to efficiently speed up the training phase with fewer parameters when using a small number of images collected for each group while maintaining favorable classification accuracy for COVID-19 detection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Huang

Liao

2022

Computers in Biology and Medicine

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Section: Discussionsupporting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Huang

Liao

2022

Computers in Biology and Medicine

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“…The cropping process aims to eliminate unnecessary noise outside the object of research by cutting each side of the object and removing areas that are not needed [20]. The results of this cut will get the area that will be needed so that the area that is not needed that is outside the area of the object of the Region of Interest (ROI) being studied can be removed [21].…”

Section: Image Croppingmentioning

confidence: 99%

Development of Mastoid Air Cell System Extraction Method on Temporal CT-scan Image

Arlis

Defit

Sumijan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Mastoiditis is disease that to infection of the mastoid bone cavity that affects the size of the air cell system of the temporal bone. Visually, the information temporal CT image mastoid bone has can assist medical experts in viewing the mastoid air cell system (MACS), but the fact that medical personnel are experiencing difficulties in determining the size MACS is due to the many different characteristics and objects overlap, so that in the measurement of the area, precise and accurate results have not been obtained. This study aims to separate the object of the MACS with the development of extraction. The proposed method uses Morphology and Regionprops operations. The dataset used in the testing process is 347 of 5 patients indicated for Mastoiditis. The results obtained can calculate the area of MACS for each test image. Based on image testing, the area of the smallest MACS in this study was 0.589 cm2 and the largest was 6.183 cm2. This, the smaller the size of the MACS indicates the severity of infection, so this study can help medical personnel make decisions and take appropriate treatment actions.

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“…Lu Xu et al [20] developed three deep learning models, CNN, LSTM and CNN, to predict the number of COVID-19 cases, and successfully predicted the spread trend in Brazil, India, and Russia. Khabir Uddin Ahamed et al [21] developed a deep learning-based COVID-19 case detection model trained on a dataset consisting of chest CT scans and X-ray images, helping radiologists use basic but widely available equipment to rapid diagnosis of COVID-19 cases. In [22], The authors focused on summarizing deep learning methods that have been significantly utilized in the automatic identification of COVID-19 cases from other lung diseases and normal populations while also discussing the challenges associated with current DL methods for COVID-19 diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 Diagnosis by Gray-level Cooccurrence Matrix and Genetic Algorithm

Jiang

Brown

et al. 2022

EAI Endorsed Trans e-Learn

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Currently, improving the identification of COVID-19 with the help of computer vision and artificial intelligence has received great attention from researchers. This paper proposes a novel method for automatic detection of COVID-19 based on chest CT to help radiologists improve the speed and reliability of tests for diagnosing COVID-19. Our algorithm is a hybrid approach based on the Gray-level Cooccurrence Matrix and Genetic Algorithm. The Gray-level Cooccurrence Matrix (GLCM) was used to extract CT scan image features, GA algorithm was used as an optimizer, and a feedforward neural network was used as a classifier. Finally, we use 296 chest CT scan images to evaluate the detection performance of our proposed method. To more accurately evaluate the accuracy of the algorithm, 10-run 10-fold cross-validation was introduced. Experimental results show that our proposed method outperforms state-of-the-art methods in terms of Sensitivity, Accuracy, F1, MCC, and FMI.

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A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

Cited by 92 publications

References 58 publications

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Development of Mastoid Air Cell System Extraction Method on Temporal CT-scan Image

Covid-19 Diagnosis by Gray-level Cooccurrence Matrix and Genetic Algorithm

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