Detecting COVID-19 from chest computed tomography scans using AI-driven android application

Verma, Aryan; Amin, Sanjiv B.; Naeem, Muhammad; Saha, Monjoy

doi:10.1016/j.compbiomed.2022.105298

Cited by 35 publications

(28 citation statements)

References 38 publications

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“…For faster and more accurate examination, many artificial intelligence (AI) techniques for automated detection and quantitative analysis of COVID-19 lesions from CT images have been developed based on deep learning and radiomics (9)(10)(11)(12)(13)(14)(15)(16)(17)(18). In addition to detecting lesions, assessing the grade of COVID-19 pulmonary lesions is important for the hierarchical management and treatment of infected patients (19).…”

Section: Introductionmentioning

confidence: 99%

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Jiang¹,

Jin²,

Han³

et al. 2022

Quant Imaging Med Surg

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Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data.Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models.Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/ severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability.Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, ^ ORCID: Zekun Jiang,

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Section: Introductionmentioning

confidence: 99%

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Jiang¹,

Jin²,

Han³

et al. 2022

Quant Imaging Med Surg

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“… COVID-CXNet(Accuracy 87.88%) Nassif et al [ 59 ],(2022) Detection CXR,Audio 1159 sound samples 13,808 CXR Image LSTM, VGG16, VGG19, DENSNET201,RESNET50, INCEPTIONV3 INCEPTIONRESNETV2, XCEPTION LSTM (Accuracy of 98%)VGG16(Accuracy 89.64%)InceptionResNetV2(Accuracy 82.22%) Nayak et al [ 60 ],(2020) Detection CXR 406 ALEXNET,VGG16,GOOGLE NET,MOBILE NET-V2,SQUEEZENET,RESNET-34, RESNET-50,INCEPTION-V3 ResNet-34 (Accuracy 98.33%). Verma et al [ 61 ],(2022) Detection CT 63,849 RESNET50 V2, EFFICIENTNET B0 EfficientNet B0 (Sensitivity 99.69%) Sim et al [ 62 ],(2022) Detection CXR 5717 DENSENET121 DenseNet121(Sensitivity 95%) Srivastava and Ruchilekha [ 63 ],(2022) Detection CXR, CT 4271 DEEPCOVX, DEEPCOVCT DeepCovX (Sensitivity 100%) DeepCovCT(Sensitivity 97.06%) Muljo [ 64 ],(2022) Detection CXR 133,280 DENSENET121 DenseNet121(AUC average of 82.16, best AUC 99.99%) Panwar et al [ 65 ],(2021) Classification CXR 4563 CNN, ALEXNET CNN (Accuracy 98%) Nasser et al [ 66 ],(2021) Detection CXR 6000 RESNET50 ResNet50(Sensitivity 97.3%) …”

Section: Analysis and Findingsmentioning

confidence: 99%

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives

Paul

Saha

Biswas

et al. 2023

Array

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“…Verma et al [ 27 ] have created an innovative Android application that uses a very efficient and accurate DL algorithm to identify COVID-19 infection from chest CT images. The model generates a TensorFlow lite flat buffer file (.tflite) which is used to decrease the model's size, and the model is optimized for speed and latency on edge devices.…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models

Saravagi¹,

Agrawal²,

Saravagi

et al. 2022

Computational Intelligence and Neuroscience

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Spondylolisthesis refers to the slippage of one vertebral body over the adjacent one. It is a chronic condition that requires early detection to prevent unpleasant surgery. The paper presents an optimized deep learning model for detecting spondylolisthesis in X-ray radiographs. The dataset contains a total of 299 X-ray radiographs from which 156 images are showing the spine with spondylolisthesis and 143 images are of the normal spine. Image augmentation technique is used to increase the data samples. In this study, VGG16 and InceptionV3 models were used for the image classification task. The developed model is optimized by utilizing the TFLite model optimization technique. The experimental result shows that the VGG16 model has achieved a 98% accuracy rate, which is higher than InceptionV3’s 96% accuracy rate. The size of the implemented model is reduced up to four times so it can be used on small devices. The compressed VGG16 and InceptionV3 models have achieved 100% and 96% accuracy rate, respectively. Our finding shows that the implemented models were outperformed in the diagnosis of lumbar spondylolisthesis as compared to the model suggested by Varcin et al. (which had a maximum of 93% accuracy rate). Also, the developed quantized model has achieved higher accuracy rate than Zebin and Rezvy’s (VGG16 + TFLite) model with 90% accuracy. Furthermore, by evaluating the model’s performance on other publicly available datasets, we have generalised our approach on the public platform.

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Detecting COVID-19 from chest computed tomography scans using AI-driven android application

Cited by 35 publications

References 38 publications

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives

Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models

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