Stage-Wise Neural Architecture Search

Jordão, Artur; Akio, Fernando; Lie, Maiko; Schwartz, William Robson

doi:10.48550/arxiv.2004.11178

2020

DOI: 10.48550/arxiv.2004.11178

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Stage-Wise Neural Architecture Search

Artur Jordão¹,

Fernando Akio²,

Maiko Lie³

et al.

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2022

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“…The inexpensive CNNbased solution is introduced to overcome the issues mentioned earlier. Additionally, due to the trial-and-error aspect of CNN, establishing a well-performing CNN requires substantial cognitive programming appropriate for a resource-constrained device [13,26] and uploading such CNN to the cloud.…”

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confidence: 99%

Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone

Lanjewar

Shaikh

Parab

2022

Multimed Tools Appl

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COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce, and is time-consuming. Coronaviruses show significant manifestation in Chest X-Ray (CX-Ray) images and, thus, can be a viable option for an alternate COVID-19 diagnostic strategy. An automatic COVID-19 detection system can be developed to detect the disease, thus reducing strain on the healthcare system. This paper discusses a real-time Convolutional Neural Network (CNN) based system for COVID-19 illness prediction from CX-Ray images on the cloud. The implemented CNN model displays exemplary results, with training accuracy being 99.94% and validation accuracy reaching 98.81%. The confusion matrix was utilized to assess the models’ outcome and achieved 99% precision, 98% recall, 99% F1 score, 100% training area under the curve (AUC) and 98.3% validation AUC. The same CX-Ray dataset was also employed to predict the COVID-19 disease with deep Convolution Neural Networks (DCNN), such as ResNet50, VGG19, InceptonV3, and Xception. The prediction outcome demonstrated that the present CNN was more capable than the DCNN models. The efficient CNN model was deployed to the Platform as a Service (PaaS) cloud.

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confidence: 99%

Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone

Lanjewar

Shaikh

Parab

2022

Multimed Tools Appl

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Convolutional neural network based tea leaf disease prediction system on smart phone using paas cloud

Lanjewar

Panchbhai²

2022

Neural Comput & Applic

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Stage-Wise Neural Architecture Search

Cited by 2 publications

References 0 publications

Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone

Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone

Convolutional neural network based tea leaf disease prediction system on smart phone using paas cloud

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