CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network

Suganyadevi, S.; Seethalakshmi, V.

doi:10.1007/s11277-022-09864-y

Cited by 38 publications

(3 citation statements)

References 114 publications

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“…It combines the advantages of both adaptive gradient descent (AdaGrad) and root mean square propagation (RMSprop) algorithms. During the optimization process, the Adam algorithm employs a learning coefficient, denoted as η (eta), which determines the step size or rate at which the weights are updated [28,29]. The learning coefficient η can vary at different time steps during the training process, denoted as t. The specific value of η at a given time step t is determined by the Adam optimization algorithm as represented an Eq.…”

Section: Training and Optimization Of The Proposed Architecturementioning

confidence: 99%

QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images

Chandrika,

Karpagam,

Richard

et al. 2024

j.electron.electromedical.eng.med.inform

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The COVID-19 pandemic has had a terrible effect on human health, and computer-aided diagnostic (CAD) systems for chest computed tomography have emerged as a potential alternative for COVID-19 diagnosis. Yet, since the cost of data annotation may be excessively costly in the medical area, there is a shortage of data that has been annotated. A considerable quantity of labelled data is required in order to train a CAD system to a high level of accuracy. The study aims to describe an automatic and precise COVID-19 diagnostic method that utilizes a restricted amount of labelled CT images to solve this problem. The framework of the system is known as Qualified Contrastive Machine Learning (QCML), and the improvements that we have made may be summed up as follows: 1) In order to make use of all of the image's characteristics, we combine features with a two-dimensional discrete wavelet transform. 2) We employ the COVID-Net encoder with a redesign that focuses on the efficiency of learning and the task specificity of the data. 3) In order to strengthen our capacity to generalize, we have implemented a novel pertaining technique that is based on Qualified Contrastive Machine Learning. 4) In order to get better categorization results, we have included an extra auxiliary work. The application of Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images offers an accuracy of 93.55%, a recall of 91.59%, a precision of 96.92%, and an F1-score of 94.18%, demonstrating the potential for accurate and efficient COVID-19 diagnosis with limited labelled data.

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Section: Training and Optimization Of The Proposed Architecturementioning

confidence: 99%

QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images

Chandrika,

Karpagam,

Richard

et al. 2024

j.electron.electromedical.eng.med.inform

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show abstract

“…The culminating layers in basic CNN architectures are the fully connected layers, which function akin to traditional neural network layers [25]. Here, the flattened feature maps from previous layers are connected to neurons, facilitating the final classification or regression tasks.…”

Section: B Basic Cnn Architecturesmentioning

confidence: 99%

Deep CNN for the Identification of Pneumonia Respiratory Disease in Chest X-Ray Imagery

Nessipkhanov,

Davletova,

Kurmanbekkyzy

et al. 2023

IJACSA

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Addressing the challenges of diagnosing lower respiratory tract infections, this study unveils the potential of Deep Convolutional Neural Networks (Deep CNN) as transformative tools in medical image interpretation. Our research presents a tailored Deep CNN model, optimized for distinguishing pneumonia in chest X-ray images, a task often complicated by subtle radiological differences. We utilized an extensive dataset comprising 12,000 chest X-rays, which incorporated both pneumonia-affected and healthy samples. Through rigorous pre-processing, encompassing noise abatement, normalization, and data augmentation, a fortified training set emerged. This set was the basis for our Deep CNN, marked by intricate convolutional designs, planned dropouts, and modern activation functions. With 85% of images used for training and the balance for validation, the model manifested an impressive 98.1% accuracy, surpassing preceding approaches. Crucially, specificity and sensitivity metrics stood at 97.5% and 98.8%, highlighting the model's precision in segregating pneumonia cases from clear ones, thus reducing diagnostic errors. These results emphasize Deep CNN's transformative capability in pneumonia diagnosis via X-rays and suggest potential applications across various medical imaging facets.However, as we champion these outcomes, we must cognizantly assess potential hurdles in clinical application, encompassing ethical deliberations, model scalability, and its adaptability to ever-changing pulmonary disease profiles.

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“…However, owing to the disease's recent origins and resemblances to other respiratory conditions such as pneumonia, proper interpretation of findings via pictures presents various difficulties. Achieving a reliable diagnosis of COVID-19 is difficult and time consuming because of its complexity [6][7][8][9][10]. Only radiologists are qualified to conduct this work.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Classification Algorithms for COVID-19 Detection

Sidheeque¹,

Sumathy²,

M³

2022

IJACSA

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The imaging modalities of chest X-rays and computed tomography (CT) are commonly utilized to quickly and accurately diagnose COVID-19. Due to time and human error, it is exceedingly difficult to manually identify the infection using radio imaging. COVID-19 identification is being mechanized and improved with the use of artificial intelligence (AI) tools that have already showed promise. This study employs the following methodology: The chest footage was pre-processed by setting equalizing the histogram, sharpening it, and so on. The transformed chest images are then retrieved through shallow and high-level feature mapping over the backbone network. To further improve the classification performance of the convolutional neural network, the model uses self-attained mechanism through feature maps. Numerous simulations show that CT image classification and augmentation may be accomplished with higher efficiency and flexibility using the Inception-Resnet convolutional neural network than with traditional segmentation methods. The experiment illustrates the association between model accuracy, model loss, and epoch. Inception-statistical Resnet's measurement results are 98%, 91%, 91%.

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CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network

Cited by 38 publications

References 114 publications

QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images

QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images

Deep CNN for the Identification of Pneumonia Respiratory Disease in Chest X-Ray Imagery

Deep Learning and Classification Algorithms for COVID-19 Detection

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