Deep Learning Based Mathematical Model for Feature Extraction to Detect Corona Virus Disease using Chest X-ray Images

Gupta, R. K.; Sahu, Yatendra; Kunhare, Nilesh; Gupta, Abhishek; Prakash, Deo

doi:10.1142/s0218488521500410

Cited by 12 publications

(5 citation statements)

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“…The succession of convolutional and pooling layers that make up the convolutional base is used to extract features from the input image. Following the transmission of these features, the fully linked layers use them to assign the image to one of the 1000 ImageNet classes [30,31]. In the next subsection, the pre-trained model is briefly described.…”

Section: Vgg-16 Architecturementioning

confidence: 99%

Hybrid Facial Emotion Recognition Using CNN-Based Features

et al. 2023

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In computer vision, the convolutional neural network (CNN) is a very popular model used for emotion recognition. It has been successfully applied to detect various objects in digital images with remarkable accuracy. In this paper, we extracted learned features from a pre-trained CNN and evaluated different machine learning (ML) algorithms to perform classification. Our research looks at the impact of replacing the standard SoftMax classifier with other ML algorithms by applying them to the FC6, FC7, and FC8 layers of Deep Convolutional Neural Networks (DCNNs). Experiments were conducted on two well-known CNN architectures, AlexNet and VGG-16, using a dataset of masked facial expressions (MLF-W-FER dataset). The results of our experiments demonstrate that Support Vector Machine (SVM) and Ensemble classifiers outperform the SoftMax classifier on both AlexNet and VGG-16 architectures. These algorithms were able to achieve improved accuracy of between 7% and 9% on each layer, suggesting that replacing the classifier in each layer of a DCNN with SVM or ensemble classifiers can be an efficient method for enhancing image classification performance. Overall, our research demonstrates the potential for combining the strengths of CNNs and other machine learning (ML) algorithms to achieve better results in emotion recognition tasks. By extracting learned features from pre-trained CNNs and applying a variety of classifiers, we provide a framework for investigating alternative methods to improve the accuracy of image classification.

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Section: Vgg-16 Architecturementioning

confidence: 99%

Hybrid Facial Emotion Recognition Using CNN-Based Features

et al. 2023

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“…X-ray scan image analysis with data augmentation and transfer learning for COVID-19 detection from multiple CNN architectures have also demonstrated the efficacy in COVID-19 diagnosis predictions [29]. For dataset imbalance and noise removal, Generative Adversarial Networks (GANs) and Principal Component Analysis (PCA) combined with transfer learning modelling approaches have also demonstrated promising results [15]. Even though X-ray modalities have more availability as compared to CT modalities, their sensitivity and specificity is found rather low medically while diagnosing earlier stages of COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images

Rana¹,

Singh²,

Mavuduru

et al. 2022

Multimed Tools Appl

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The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score.

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“…However, it is good to evaluate on multiple datasets before arriving at a conclusion about the performance of a model. Models [7,13,18,23,24,30,33,35,48,60,74] were trained for multiple classifications; however, the single best performing pre-trained model was considered as the best model. A few models combined features from different pre-trained models [17,27,53], but there is no guarantee that the the highlighted combination was the best one.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images

Jangam

Annavarapu

Barreto

2022

Multimed Tools Appl

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To accurately diagnose multiple lung diseases from chest X-rays, the critical aspect is to identify lung diseases with high sensitivity and specificity. This study proposed a novel multi-class classification framework that minimises either false positives or false negatives that is useful in computer aided diagnosis or computer aided detection respectively. To minimise false positives or false negatives, we generated respective stacked ensemble from pre-trained models and fully connected layers using selection metric and systematic method. The diversity of base classifiers was based on diverse set of false positives or false negatives generated. The proposed multi-class framework was evaluated on two chest X-ray datasets, and the performance was compared with the existing models and base classifiers. Moreover, we used LIME (Local Interpretable Model-agnostic Explanations) to locate the regions focused by the multi-class classification framework.

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Deep Learning Based Mathematical Model for Feature Extraction to Detect Corona Virus Disease using Chest X-ray Images

Cited by 12 publications

References 0 publications

Hybrid Facial Emotion Recognition Using CNN-Based Features

Hybrid Facial Emotion Recognition Using CNN-Based Features

Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images

A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images

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