Optimized NASNet for Diagnosis of COVID-19 from Lung CT Images

Bharati, Subrato; Podder, Prajoy; Mondal, M. Rubaiyat Hossain; Gandhi, Niketa

doi:10.1007/978-3-030-71187-0_59

Cited by 19 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several researchers have proven accurate and effective respiratory disease diagnosis deploying chest radiology and DL [32,39]. Recent DL models such as NASNet can also effectively classify the COVID-19 patients from CT scanned images [33]. Owing to a small number of radiologists for the massive number of coronavirus affected patients, AI-based diagnosis system is necessary for correctly diagnosis and to manage the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

“…A recent work [33] developed a model named COVIDX-Net. This work contained a comparative study of various DL models including ResNetV2, VGG19, InceptionV3, DenseNet201, MobileNetV2, Xception, and InceptionResNetV2 for binary classification.…”

Section: Introductionmentioning

confidence: 99%

“…This work contained a comparative study of various DL models including ResNetV2, VGG19, InceptionV3, DenseNet201, MobileNetV2, Xception, and InceptionResNetV2 for binary classification. They used 50 data samples of X-ray where 25 samples had COVID-19 and 25 samples had normal patients [33]. The achieving results of experiments were for two databases of COVID-19 X-ray [34].…”

Section: Introductionmentioning

confidence: 99%

“…It contains 123 X-ray images of lung anterior view and the data of [35]. The achieving accuracy was 90% for both DenseNet201 and VGG19 [33]. However, their using dataset was small, which was a limitation of this work.…”

Section: Introductionmentioning

confidence: 99%

“…The highest accuracy was 98.75% for binary classification, where multiclass provided the highest accuracy of 93.48% [41]. In earlier studies [33,36], VGG19 provided better results than other models with respect to accuracy. Next, the authors of [44] applied SVM and ResNet50 for COVID-19 classification deploying X-ray images.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

Bharati

Podder

Mondal

et al. 2021

HIS

Self Cite

View full text Add to dashboard Cite

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

Bharati

Podder

Mondal

et al. 2021

HIS

Self Cite

View full text Add to dashboard Cite

show abstract

Grayscale image statistics of COVID‐19 patient CT scans characterize lung condition with machine and deep learning

Ghashghaei

Wood

Sadatshojaei

et al. 2022

Chronic Diseases and Translational Medicine

View full text Add to dashboard Cite

Background Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID‐19. Method Five hundred thirteen CT images relating to 57 patients (49 with COVID‐19; 8 free of COVID‐19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID‐19‐related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes. Results The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians’ visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians’ assessments (99.81% AS accuracy; 1 error from 513 images). Conclusion Grayscale CT image attributes can be successfully used to distinguish the severity of COVID‐19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.

show abstract