Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID‐19 in CT Images

Oluwasanmi, Ariyo; Aftab, Muhammad Umar; Qin, Zhiguang; Son, Ngo Tung; Doan, Thang Van; Nguyen, Son Ba; Nguyen, Son Hoang

doi:10.1155/2021/6680455

Cited by 16 publications

(13 citation statements)

References 37 publications

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“…The authors of [17] studied the classification of CT scans based on TL using five pre-trained networks: DenseNet, Inception, MobileNet, ResNet50, and VGG16. The TL was adopted in these networks to enhance the performance as the authors worked on small datasets in order to eliminate the network training from scratch and avoid the large complexity of training.…”

Section: Related Workmentioning

confidence: 99%

Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models

El-Shafai¹,

Algarni²,

Banby³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Automated diagnosis based on medical images is a very promising trend in modern healthcare services. For the task of automated diagnosis, there should be flexibility to deal with an enormous amount of data represented in the form of medical images. In addition, efficient algorithms that could be adapted according to the nature of images should be used. The importance of automated medical diagnosis has been maximized with the evolution of COVID-19 pandemic. COVID-19 first appeared in China, Wuhan, and then it has exploded in the whole world with a very bad impact on our daily life. The third wave of COVID-19 in the third world is really a disaster in current days, especially with the emergence of the delta variant of COVID-19 that is widespread. Required inspections should be carried out to monitor the COVID-19 spread in daily life and allow primary diagnosis of suspected cases, and long-term clinical laboratory monitoring. Healthcare professionals or radiologists can exploit AI (Artificial Intelligence) tools to quickly and reliably identify the cases of COVID-19. This paper introduces a DCNN (Deep Convolutional Neural Network) framework for chest X-ray and CT image classification based on TL (Transfer Learning). The objective is to perform multi-class and binary classification of the images in order to determine pneumonia and COVID-19 case. The TL is feasible, when using a small dataset by transferring knowledge from natural image classification to medical image classification. Two types of TL are used. The first type is finetuning of the DenseNet121, Densenet169, DenseNet201, ResNet50, ResNet152, VGG16, and VGG19 models. The second type is deep tuning of the LeNet-5, AlexNet, Inception naïve v1, and VGG16 models. Extensive tests have been carried out on datasets of chest X-ray and CT images with different training/testing ratios of 80%:20%, 70%:30%, and 60%:40%. Experimental results on 9,270 chest X-ray ray and 2,762 chest CT images acquired from different institutions show that the TL is effective with an average accuracy of 98.49%.

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Section: Related Workmentioning

confidence: 99%

Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models

El-Shafai¹,

Algarni²,

Banby³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…Elhia et al [35] proposed a nonlinear model to control coronavirus spreading in Morocco city. Oluwasanmi et al [36] demonstrated the use of deep learning and adversarial network to detect COVID-19 pneumonia in computed tomography scans of the lungs. ey obtained very satisfactory results.…”

Section: Complexitymentioning

confidence: 99%

Impact of Weather Predictions on COVID‐19 Infection Rate by Using Deep Learning Models

et al. 2021

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Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model.

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“…Eight pre-trained CNN models were assessed for this purpose. In [13], the authors presented transfer learning and the adversarial network on CT scans to annotate COVID-19 and Pneumonia images. An alternative modeling system described as DeTraC was suggested in [14].…”

Section: Introductionmentioning

confidence: 99%

Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN Models

El‐Shafai¹,

Ali²,

El-Rabaie³

et al. 2022

Computers, Materials &Amp; Continua

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In developing countries, medical diagnosis is expensive and time consuming. Hence, automatic diagnosis can be a good cheap alternative. This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks (CNNs). These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists. The deep CNNs allow direct learning from the medical images. However, the accessibility of classified data is still the largest challenge, particularly in the field of medical imaging. Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image classification. However, because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19, transfer learning is not usually a robust solution. Single-Image Super-Resolution (SISR) can facilitate learning to enhance computer vision functions, apart from enhancing perceptual image consistency. Consequently, it helps in showing the main features of images. Motivated by the challenging dilemma of Pneumonia and COVID-19 diagnosis, this paper introduces a hybrid CNN model, namely SIGTra, to generate super-resolution versions of X-ray and CT images. It depends on a Generative Adversarial Network (GAN) for the super-resolution reconstruction problem. Besides, Transfer learning with CNN (TCNN) is adopted for the classification of images. Three different categories of chest X-ray and CT images can be classified with the proposed model. A comparison study is presented between the proposed SIGTra model and the other related CNN models for COVID-19 detection in terms of precision, sensitivity, and accuracy.

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Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID‐19 in CT Images

Cited by 16 publications

References 37 publications

Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models

Classification Framework for COVID-19 Diagnosis Based on Deep CNN Models

Impact of Weather Predictions on COVID‐19 Infection Rate by Using Deep Learning Models

Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN Models

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