Multi-modal fusion of deep transfer learning based COVID-19 diagnosis and classification using chest x-ray images

Reddy, A. Siva Krishna; Rao, K. Nageswara; Soora, Narasimha Reddy; Kotte, Shailaja; Kumar, Nalini; Sridharan, Abel; Uthayakumar, J.

doi:10.1007/s11042-022-13739-6

Cited by 15 publications

(7 citation statements)

References 22 publications

(14 reference statements)

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“…The outcomes represented by the SCODL-DDC approach show its ability in classifying various classes. The comparison study of the SCODL-DDC technique with other COVID-19 classifiers is given in Table 3 [34]. Based on sens y , the SCODL-DDC technique reaches an increasing sens y of 99.65% while the fusion, Inception v3, ResNet-50, VGG-16, DLS-SCD, DLA-CVD, AD-TLCNN, and FM-HCF-DLF models attain a decreasing sens y of 92.86%, 94.26%, 88.17%, 86.57%, 86.21%, 87.22%, 99.46%, and 93.59%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images

2023

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Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images

2023

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“…A. Siva Krishna Reddy et al [18] The MMF-DTL model is utilized to reduce the diagnosis procedure and manage the current epidemic.…”

Section: Soarov Chakraborty Et Al [17]mentioning

confidence: 99%

“…This section illustrates the comparative analysis of the proposed DECWOA-CNN model with performance metrics like accuracy, precision, sensitivity, specificity and f1-score as shown in Table 4.5. The existing result such as [15] [16], [17], [18] and [20] are utilized for estimating an ability of the classifier. The DECWOA-CNN is trained, tested and validated by using CXR dataset.…”

Section: Comparative Analysismentioning

confidence: 99%

Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network

Manoj Kumar,

N Patil,

Bidare Divakarachari

et al. 2024

SCPE

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COVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a Differential Evolution Chaotic Whale Optimization Algorithm (DECWOA) based Convolutional Neural Network (CNN) method for identifying and classifying COVID-19 chest X-ray images. The DECWOA based CNN model improves the accuracy and convergence speed of the algorithm. This method is evaluated {by} Chest X-Ray (CXR) dataset and attains better results in terms of accuracy, precision, sensitivity, specificity, and F1-score values of about 99.89}%, 99.83%, 99.81%, 98.92%, and 99.26% correspondingly. The result shows that the proposed DECWOA based CNN model provides accurate and quick identification and classification of COVID-19 compared to existing techniques like ResNet50, VGG-19, and Multi-Model Fusion of Deep Transfer Learning (MMF-DTL) models.

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“…The system attained an accuracy of 96.83% and shows promising potential for aiding radiologists in COVID diagnosis. In the work proposed by Reddy et al [37], a multi-modal fusion technique was introduced using a deep transfer learning approach. The proposed technique comprises three stages: pre-processing, feature extraction, and classification.…”

Section: Related Workmentioning

confidence: 99%

An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

Khan¹,

Rehman²,

Ahmed³

et al. 2023

Computer Systems Science and Engineering

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In 2020, COVID-19 started spreading throughout the world. This deadly infection was identified as a virus that may affect the lungs and, in severe cases, could be the cause of death. The polymerase chain reaction (PCR) test is commonly used to detect this virus through the nasal passage or throat. However, the PCR test exposes health workers to this deadly virus. To limit human exposure while detecting COVID-19, image processing techniques using deep learning have been successfully applied. In this paper, a strategy based on deep learning is employed to classify the COVID-19 virus. To extract features, two deep learning models have been used, the DenseNet201 and the SqueezeNet. Transfer learning is used in feature extraction, and models are fine-tuned. A publicly available computerized tomography (CT) scan dataset has been used in this study. The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm. The proposed technique is validated through multiple evaluation parameters. Several classifiers have been employed to classify the optimized features. The cubic support vector machine (Cubic SVM) classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%. The proposed technique achieves state-of-the-art accuracy, a sensitivity of 98.80%, and a specificity of 96.64%.

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Multi-modal fusion of deep transfer learning based COVID-19 diagnosis and classification using chest x-ray images

Cited by 15 publications

References 22 publications

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images

Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images

Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network

An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

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