Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images

Hossain, Md. Belal; Iqbal, Shahriar; Islam, Md. Monirul; Akhtar, Md. Nasim; Sarker, Iqbal H.

doi:10.1016/j.imu.2022.100916

Cited by 68 publications

(36 citation statements)

References 56 publications

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“…Belal Hossain et al [ 16 ] developed a transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images. In this paper, the authors present a method for how to identify the presence of COVID-19 in X-ray images, using transfer learning (TL) on a ResNet50 model.…”

Section: State-of-the-art Methodsmentioning

confidence: 99%

COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images

et al. 2022

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The novel coronavirus 2019 (COVID-19) spread rapidly around the world and its outbreak has become a pandemic. Due to an increase in afflicted cases, the quantity of COVID-19 tests kits available in hospitals has decreased. Therefore, an autonomous detection system is an essential tool for reducing infection risks and spreading of the virus. In the literature, various models based on machine learning (ML) and deep learning (DL) are introduced to detect many pneumonias using chest X-ray images. The cornerstone in this paper is the use of pretrained deep learning CNN architectures to construct an automated system for COVID-19 detection and diagnosis. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pre-trained CNN models, AlexNet and Xception. Hence, we propose COVID-AleXception: a neural network that is a concatenation of the AlexNet and Xception models for the overall improvement of the prediction capability of this pandemic. To evaluate the proposed model and build a dataset of large-scale X-ray images, there was a careful selection of multiple X-ray images from several sources. The COVID-AleXception model can achieve a classification accuracy of 98.68%, which shows the superiority of the proposed model over AlexNet and Xception that achieved a classification accuracy of 94.86% and 95.63%, respectively. The performance results of this proposed model demonstrate its pertinence to help radiologists diagnose COVID-19 more quickly.

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Section: State-of-the-art Methodsmentioning

confidence: 99%

COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images

et al. 2022

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“…Overview of the Proposed Method. As reported in References [19] and [20], the ResNet50 model can be used as a useful and powerful tool for many biomedical applications, such as the detection of COVID-19 from chest X-ray images and the diagnosis in 12-lead electrocardiogram. In this article, a fine-tuning ResNet50 model was performed and tested for the automatic detection of superficial cracks in the cracked tooth.…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Cracks in Cracked Tooth Based on Binary Classification Convolutional Neural Networks

Guo

Chen

et al. 2022

Applied Bionics and Biomechanics

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Cracked tooth syndrome is a commonly encountered disease in dentistry, which is often accompanied by dramatic painful responses from occlusion and temperature stimulation. Current clinical diagnostic trials include traditional methods (such as occlusion test, probing, cold stimulation, etc.) and X-rays based medical imaging (periapical radiography (PR), cone-beam computed tomography (CBCT), etc.). However, these methods are strongly dependent on the experience of the clinicians, and some inconspicuous cracks are also extremely easy to be overlooked by visual observation, which will definitely affect the subsequent treatments. Inspired by the achievements of applying deep convolutional neural networks (CNNs) in crack detection in engineering, this article proposes an image-based crack detection method using a deep CNN classifier in combination with a sliding window algorithm. A CNN model is designed by modifying the size of the input layer and adding a fully connected layer with 2 units based on the ResNet50, and then, the proposed CNN is trained and validated with a self-prepared cracked tooth dataset including 20,000 images. By comparing validation accuracy under seven different learning rates, 10 − 5 is chosen as the best learning rate for the following testing process. The trained CNN is tested on 100 images with 1920 × 1080 -pixel resolutions, which achieves an average accuracy of 90.39%. The results show that the proposed method can effectively detect cracks in images under various conditions (stained, overexplosion, images affected by other diseases). The proposed method in this article provides doctors with a more intelligent diagnostic solution, and it is not only suitable for optical photographs but also for automated diagnosis of other medical imaging images.

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“…The development of CNN has altered how image classification issues are handled [18]. MobileNetV2 [19], ResNet50 [20], DenseNet169 [21], and InceptionResNetV2 [22] are four kinds of CNN architecture employed in this paper to identify diseases in Robusta coffee leaves. Following is a concise explanation of the CNN architecture used in this research.…”

Section: Model Convolutional Neural Networkmentioning

confidence: 99%

Robusta coffee leaf diseases detection based on MobileNetV2 model

Aufar¹,

Kaloka²

2022

IJECE

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<span lang="EN-US">Indonesia is a major exporter and producer of coffee, and coffee cultivation adds to the nation's economy. Despite this, coffee remains vulnerable to several plant diseases that may result in significant financial losses for the agricultural industry. Traditionally, plant diseases are detected by expert observation with the naked eye. Traditional methods for managing such diseases are arduous, time-consuming, and costly, especially when dealing with expansive territories. Using a model based on transfer learning and deep learning model, we present in this study a technique for classifying Robusta coffee leaf disease photos into healthy and unhealthy classes. The MobileNetV2 network serves as the model since its network design is simple. Therefore, it is likely that the suggested approach will be deployed further on mobile devices. In addition, the transfer learning and experimental learning paradigms. Because it is such a lightweight net, the MobileNetV2 system serves as the foundational model. Results on Robusta coffee leaf disease datasets indicate that the suggested technique can achieve a high level of accuracy, up to 99.93%. The accuracy of other architectures besides MobileNetV2 such as DenseNet169 is 99.74%, ResNet50 architecture is 99.41%, and InceptionResNetV2 architecture is 99.09%.</span>

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Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images

Cited by 68 publications

References 56 publications

COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images

COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images

Automatic Detection of Cracks in Cracked Tooth Based on Binary Classification Convolutional Neural Networks

Robusta coffee leaf diseases detection based on MobileNetV2 model

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