Transfer Learning for Detection of COVID-19 Infection using Chest X-Ray Images

Bhatia, Nikhil; Bhola, Geetanjali

doi:10.1109/iccmc51019.2021.9418398

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…In the work of Bhatia and Bhola [27], various radiographic characteristics in different situations can be detected using transfer learning with a range of pretrained architectures. Upon evaluating the pre-trained architectures, Squeezenet1.1, AlexNet, DenseNet-121, and GoogleNet were identified as the top-performing models for recognizing coronavirus in chest X-rays.…”

Section: Literature Surveymentioning

confidence: 99%

CovidSafe: A Deep Learning Framework for Covid Detection Using Multi-Modal Approach

Panigrahi,

Behera,

Routhu

2024

Preprint

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This work introduces CovidSafe, a framework designed to classify COVID patients as positive or negative based on CXR images. With the global impact of COVID-19, there is a need for effective tools to aid in patient diagnosis. CovidSafe utilizes a multi-model deep feature extractor, combining features from the ResNet family and an autoencoder. The ResNet 18, ResNet 34, and ResNet 50 architectures are employed, and model weights are fine-tuned for optimal performance. The framework leverages the ResNet family models and theautoencoder to extract relevant features. The features from intermediate layers in Resnet family are extracted to capture increasingly complex and high-level information. Similarly, the bottleneck features from Autoencoder are extracted to capture compressed, abstract representation and important features of the image for the classification. These features are then utilized for analysis and classification within the CovidSafe framework. To ensure accessibility for online users, the proposed model is integrated into a web application. Through experimental analysis, the performance of the proposed model is evaluated with various metrics. Results indicate that CovidSafe outperforms baseline models, achieving a precision of 98.67%, recall of 98.62%, and F1-score of 98.64%. These promising results highlight the efficacy of CovidSafe in accurately classifying COVID patients based on CXR images, offering a valuable tool in the fight against the ongoing pandemic.

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Section: Literature Surveymentioning

confidence: 99%

CovidSafe: A Deep Learning Framework for Covid Detection Using Multi-Modal Approach

Panigrahi,

Behera,

Routhu

2024

Preprint

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“…The datasets consisted of two datasets, one for CXR (called DS_X9K) and one for CT scan images (called DS_CT9K), each containing 9000 images evenly divided into three classes: COVID-19, different types of pneumonia, and normal. Bhatia et al [16] discuss about the performance of several pretrained architectures which were reviewed for detecting the radiographic features in X-rays. After analyzing the pre-trained architectures, Squeezenet1.1, AlexNet, DenseNet-121, GoogleNet were better models than the others in identifying the coronavirus in the chest X-rays.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models

2024

Preprint

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Accurate diagnosis of COVID-19 is critical for patient management and disease control. In this study, we evaluate the performance of Convolutional Neural Network (CNN) models, including VGG, ResNet, Inception, and Xception, for COVID-19 detection using CT scans and chest X-ray images. Leveraging deep learning algorithms and multiple layers such as Conv2D, MaxPooling2D, Flatten, and Dense, we analyze medical images to identify COVID-19 patterns. Through comprehensive dataset training and evaluation, we assess model accuracy, sensitivity, and specificity. Our findings highlight the potential of CNN-based approaches for accurate COVID-19 diagnosis from chest radiography images, contributing to the development of advanced diagnostic tools in combating the pandemic.

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“…Consequently, some previous studies produced that the small batch size is an essential factor and has a major impact on the accuracy of the model while another study [40] gained the best accuracy by using 64 batch size. Thus, in this study, we will measure the impact of batch size and dataset size on the accuracy.…”

Section: Related Workmentioning

confidence: 99%

Using Deep Learning Algorithms to Diagnose Coronavirus Disease (COVID-19)

Alanazi¹,

Kotb²

2023

IJACSA

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With the rapid development in the area of Machine Learning (ML) and Deep learning, it is important to exploit these tools to contribute to mitigating the effects of the coronavirus pandemic. Early diagnosis of the presence of this virus in the human body can be crucially helpful to healthcare professionals. In this paper, three well-known Convolutional Neural Network deep learning algorithms (VGGNet 16, GoogleNet and ResNet50) are applied to measure their ability to distinguish COVID-19 patients from other patients and to evaluate the best performance among these algorithms with a large dataset. Two stages are conducted, the first stage with 14994 x-ray images and the second one with 33178. Each model has been applied with different batch sizes 16, 32 and 64 in each stage to measure the impact of data size and batch size factors on the accuracy results. The second stage achieved accuracy better than the first one and the 64 batch size gain best results than the 16 and 32. ResNet50 achieves a high rate of 99.31, GoogleNet model achieves 95.55, while VGG16 achieves 96.5. Ultimately, the results affect the process of expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, and resulting in improved clinical outcomes.

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Transfer Learning for Detection of COVID-19 Infection using Chest X-Ray Images

Cited by 7 publications

References 22 publications

CovidSafe: A Deep Learning Framework for Covid Detection Using Multi-Modal Approach

CovidSafe: A Deep Learning Framework for Covid Detection Using Multi-Modal Approach

Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models

Using Deep Learning Algorithms to Diagnose Coronavirus Disease (COVID-19)

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