Efficient Classification Approach Based on COVID-19 CT Images Analysis with Deep Features

Kamel, Mostafa; Abdelshafy, Mohamed; AbdulRazek, Mustafa; Abouelkhir, Osama; Fawzy, A. E.; Sahlol, Ahmed T.

doi:10.1109/icccis51004.2021.9397189

Cited by 9 publications

(2 citation statements)

References 15 publications

(12 reference statements)

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“…These models proved to be useful with transfer learning, as they were pre-trained on the ImageNet database. Various improvements to the proven Deep CNNs were presented in [20][21][22][23] proposed spiking neural networks, which provided a F1 score of 0.74 for the CT scan database. These studies utilize a single CNN classifier which can overfit the test database, resulting in high variance and low generalization on real-world systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Feature Meta-Learners Ensemble Models for COVID-19 CT Scan Classification

Thomas

Mohammed

et al. 2023

Electronics

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The infectious nature of the COVID-19 virus demands rapid detection to quarantine the infected to isolate the spread or provide the necessary treatment if required. Analysis of COVID-19-infected chest Computed Tomography Scans (CT scans) have been shown to be successful in detecting the disease, making them essential in radiology assessment and screening of infected patients. Single-model Deep CNN models have been used to extract complex information pertaining to the CT scan images, allowing for in-depth analysis and thereby aiding in the diagnosis of the infection by automatically classifying the chest CT scan images as infected or non-infected. The feature maps obtained from the final convolution layer of the Deep CNN models contain complex and positional encoding of the images’ features. The ensemble modeling of these Deep CNN models has been proved to improve the classification performance, when compared to a single model, by lowering the generalization error, as the ensemble can meta-learn from a broader set of independent features. This paper presents Deep Ensemble Learning models to synergize Deep CNN models by combining these feature maps to create deep feature vectors or deep feature maps that are then trained on meta shallow and deep learners to improve the classification. This paper also proposes a novel Attentive Ensemble Model that utilizes an attention mechanism to focus on significant feature embeddings while learning the Ensemble feature vector. The proposed Attentive Ensemble model provided better generalization, outperforming Deep CNN models and conventional Ensemble learning techniques, as well as Shallow and Deep meta-learning Ensemble CNNs models. Radiologists can use the presented automatic Ensemble classification models to assist identify infected chest CT scans and save lives.

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Section: Introductionmentioning

confidence: 99%

Deep Feature Meta-Learners Ensemble Models for COVID-19 CT Scan Classification

Thomas

Mohammed

et al. 2023

Electronics

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“…Image-based medical diagnostics can save a lot of time when it comes to detecting COVID-19, which can help to limit and prevent the spread of the virus. e major forms of pictures that can play a big role in defeating COVID-19 are CXR and CT [4]. e convolutional layer, which acts on a localized area rather than all over, is used in the first portion of CNN.…”

Section: Introductionmentioning

confidence: 99%

Discriminating the Pneumonia-Positive Images from COVID-19-Positive Images Using an Integrated Convolutional Neural Network

Vetrithangam

Indira

Umar

et al. 2022

Mathematical Problems in Engineering

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One of the most pressing issues in the current COVID-19 pandemic is the early detection and diagnosis of COVID-19, as well as the precise separation of non-COVID-19 cases at the lowest possible cost and during the disease's early stages. Deep learning-based models have the potential to provide an accurate and efficient approach for the identification and diagnosis of COVID-19, with considerable increases in sensitivity, specificity, and accuracy when used in the processing of modalities. COVID-19 illness is difficult to detect and recognize since it is comparable to pneumonia. The main objective of this study is to distinguish between COVID-19-positive images and pneumonia-positive images. We have proposed an integrated convolutional neural network focused on discriminating against COVID-19-infected patients and pneumonia patients. Preprocessing is done on the image datasets. The novelty of this research work is to differentiate the COVID-19 images from the pneumonia images. It will help the medical experts in the decision-making. In order to train the model, the image is given directly as input to integrated convolutional neural network architecture; after training the model, the system is integrated with three different kinds of datasets: COVID-19 image dataset, RSNA pneumonia dataset, and a new dataset created from COVID-19 image dataset. The attainment of the system is evaluated by calculating the measures of sensitivity, specificity, precision, and accuracy, and this system produces the accuracy values of 94.04%, 97.2%, and 97.5% for the above datasets, respectively.

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