Transfer Learning Method with Deep Residual Network for COVID-19 Diagnosis Using Chest Radiographs Images

Komal, Ayesha; Malik, Hassaan

doi:10.1007/978-981-16-7618-5_13

Cited by 6 publications

(12 citation statements)

References 27 publications

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“…It achieved an accuracy of 77.5% and a sensitivity of 75.2%, both of which are higher than those of the other two experiments. According to the results of the performance study of RF, the MLP model is superior to various other models [65][66][67][68][69][70][71][72]. We performed a 15fold LOO to further test the outcomes of the proposed model, and the result validates the relevance of MLP, as can be shown in Table 3.…”

Section: Discussionmentioning

confidence: 78%

“…This research study is comprised of five different processes: the acquisition of data from the PDB, the extraction of RNA characteristics, the training model, the prediction of probable aptamers, and the analysis of the results. One dataset was retrieved from the PDB, while the other was a benchmark dataset taken from [13] [70]. During the training process, many RNA characteristics were retrieved for the model.…”

Section: Discussionmentioning

confidence: 99%

“…The window is considered positive if the middle of the window binds the protein nucleotide with (+) instance, and the window containing non-binding nucleotide is considered negative. We also removed the feature vectors that were neither (+) nor (-) from the training phase because it may produce severely unbalanced instances for training, unless and until the initial and final windows were supposed as (+) if they consist of a protein-binding nucleotide in any location of the sliding window [68][69][70][71][72]. This is due to the limited number of (+) instances than (-) in the training database.…”

Section: Endmentioning

confidence: 99%

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Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

Mahmood¹,

Malik²,

Khan³

et al. 2022

JCBI

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Aptamers are short strands of nucleic acid with a single strand that may unite to target a certain molecule in a selective and specific manner. SELEX experiments are the typical method used for identifying aptamers in vitro (systematic evolution of ligands by exponential enrichment). Several different computational methods have been developed to locate aptamers. The purpose of this research is to identify and make predictions on the possible RNA aptamers that may be used to target the protein. To do this, we propose the use of a multi-layer perceptron neural network with sixteen layers that are trained to locate possible aptamers of a protein target. This network is trained by extracting the main properties of RNA sequences. The outcome of our proposed model is compared to the output of two well-known machine learning classifiers, namely random forest (RF) and support vector machine (SVM). Additionally, we undertake the independent testing of our model on the benchmark dataset, which allows us to reach the highest accuracy possible. As a consequence of this, our model obtains an accuracy of 98.44% and an MCC of 0.9123 during the 15-fold cross-validation, and it achieves an accuracy of 98.10% and an MCC of 0.9354 when the leave-one-out cross-validation is performed. We are certain that our approach will contribute to a reduction in the amount of money and time spent on in vitro testing. Therefore, restricting the length of the initial pool of potential nucleic acid pattern combinations.

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Section: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Endmentioning

confidence: 99%

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Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

Mahmood¹,

Malik²,

Khan³

et al. 2022

JCBI

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“…A large number of scholars are investigating a variety of approaches to cut down on the duration of communication cycles [ 22 , 23 ]. Most of the studies [ 24 , 25 , 26 , 27 , 28 ] used a technique of aggregation, such as aggregation scheduling [ 20 ], asynchronous aggregation [ 21 ], and selective aggregation [ 14 ]. Moreover, compression techniques are employed to lower the model’s communication expenses between clients and servers communicate parameters and gradients to the central server [ 29 ].…”

Section: Literature Reviewmentioning

confidence: 99%

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays

Malik

Naeem

Loh

2023

Sensors

Self Cite

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Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients’ right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model’s accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

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“…Today, one of the most economical and conventional clinical diagnostic methods is chest radiography (chest x-rays) [ 78 ]. Even in impoverished countries, modern digital radiography machines are easily affordable [ 53 ]. Hence, this practice is broadly used by health experts in their medical practices for the detection and diagnosis of fatal diseases such as TB, pulmonary nodules, pneumonia, and the initial stage of LC [ 104 ].…”

Section: Introductionmentioning

confidence: 99%

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

Malik

Anees

din

et al. 2022

Multimed Tools Appl

Self Cite

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Coronavirus (COVID-19) has adversely harmed the healthcare system and economy throughout the world. COVID-19 has similar symptoms as other chest disorders such as lung cancer (LC), pneumothorax, tuberculosis (TB), and pneumonia, which might mislead the clinical professionals in detecting a new variant of flu called coronavirus. This motivates us to design a model to classify multi-chest infections. A chest x-ray is the most ubiquitous disease diagnosis process in medical practice. As a result, chest x-ray examinations are the primary diagnostic tool for all of these chest infections. For the sake of saving human lives, paramedics and researchers are working tirelessly to establish a precise and reliable method for diagnosing the disease COVID-19 at an early stage. However, COVID-19’s medical diagnosis is exceedingly idiosyncratic and varied. A multi-classification method based on the deep learning (DL) model is developed and tested in this work to automatically classify the COVID-19, LC, pneumothorax, TB, and pneumonia from chest x-ray images. COVID-19 and other chest tract disorders are diagnosed using a convolutional neural network (CNN) model called CDC Net that incorporates residual network thoughts and dilated convolution. For this study, we used this model in conjunction with publically available benchmark data to identify these diseases. For the first time, a single deep learning model has been used to diagnose five different chest ailments. In terms of classification accuracy, recall, precision, and f1-score, we compared the proposed model to three CNN-based pre-trained models, such as Vgg-19, ResNet-50, and inception v3. An AUC of 0.9953 was attained by the CDC Net when it came to identifying various chest diseases (with an accuracy of 99.39%, a recall of 98.13%, and a precision of 99.42%). Moreover, CNN-based pre-trained models Vgg-19, ResNet-50, and inception v3 achieved accuracy in classifying multi-chest diseases are 95.61%, 96.15%, and 95.16%, respectively. Using chest x-rays, the proposed model was found to be highly accurate in diagnosing chest diseases. Based on our testing data set, the proposed model shows significant performance as compared to its competitor methods. Statistical analyses of the datasets using McNemar’s, and ANOVA tests also showed the robustness of the proposed model.

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Transfer Learning Method with Deep Residual Network for COVID-19 Diagnosis Using Chest Radiographs Images

Cited by 6 publications

References 27 publications

Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

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