Contemporary Study on Deep Neural Networks to Diagnose COVID-19 Using Digital Posteroanterior X-ray Images

Akbar, Saad; Tariq, Humera; Fahad, Muhammad; Ahmed, Ghufran; Syed, Hassan Jamil

doi:10.3390/electronics11193113

Cited by 4 publications

(5 citation statements)

References 23 publications

(118 reference statements)

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“…( 1), the accuracy is the number of correct classifications (i.e., TP and TN) divided by the total number of classifications. (10) 2) Precision: It calculates how many cases classified as positive (infection) by the model was supposed to be indicated as positive. It is defined as the ratio of classified TPs to the total number of classified positives (correctly (TP) and incorrectly (FP).…”

Section: A Evaluation Metricsmentioning

confidence: 99%

“…This means applying the same preprocessing on CXR images developing them and then designing a new model in DL for COVID-19 diseases. Several authors (such as in [4,10]) utilized only CNN to classify COVID-19 patients and didn"t achieve more accurate accuracy. www.ijacsa.thesai.org However, CNN alone is insufficient for reliable COVID-19 detection [6,11].…”

Section: Introductionmentioning

confidence: 99%

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Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

Ali¹,

Zghal²,

Hariri³

et al. 2023

IJACSA

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In the last three years, the coronavirus (COVID-19) pandemic put healthcare systems worldwide under tremendous pressure. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing many diseases (for example, COVID-19). Recently, intelligent systems (Machine Learning (ML) and Deep Learning (DL)) have been widely utilized to identify COVID-19 from other upper respiratory diseases (such as viral pneumonia and lung opacity). Nevertheless, identifying COVID-19 from the CXR images is challenging due to similar symptoms. To improve the diagnosis of COVID-19 using CXR images, this article proposes a new deep neural network model called Fast Hybrid Deep Neural Network (FHDNN). FHDNN consists of various convolutional layers and various dense layers. In the beginning, we preprocessed the dataset, extracted the best features, and expanded it. Then, we converted it from two dimensions to one dimension to reduce training speed and hardware requirements. The experimental results demonstrate that preprocessing and feature expansion before applying FHDNN lead to better detection accuracy and reduced speedy execution. Furthermore, the model FHDNN outperformed the counterparts by achieving an accuracy of 99.9%, recall of 99.9%, F1-Score has 99.9%, and precision of 99.9% for the detection and classification of COVID-19. Accordingly, FHDNN is more reliable and can be considered a robust and faster model in COVID-19 detection.

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Section: A Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

Ali¹,

Zghal²,

Hariri³

et al. 2023

IJACSA

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“…Akbar et al [105] write about the benefits of using machine learning and deep learning together to find and get rid of COVID-19. They discuss CNN's use of pre-trained, wellknown, cutting-edge deep-learning models.…”

Section: A Medical Diagnosis Of Covid-19 Using Chest X-ray Imagesmentioning

confidence: 99%

A Lightweight CNN and Class Weight Balancing on Chest X-ray Images for COVID-19 Detection

et al. 2022

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In many locations, reverse transcription polymerase chain reaction (RT-PCR) tests are used to identify COVID-19. It could take more than 48 h. It is a key factor in its seriousness and quick spread. Images from chest X-rays are utilized to diagnose COVID-19. Which generally deals with the issue of imbalanced classification. The purpose of this paper is to improve CNN’s capacity to display Chest X-ray pictures when there is a class imbalance. CNN Training has come to an end while chastening the classes for using more examples. Additionally, the training data set uses data augmentation. The achievement of the suggested method is assessed on an image’s two data sets of chest X-rays. The suggested model’s efficiency was analyzed using criteria like accuracy, specificity, sensitivity, and F1 score. The suggested method attained an accuracy of 94% worst, 97% average, and 100% best cases, respectively, and an F1-score of 96% worst, 98% average and 100% best cases, respectively.

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“…Contemporary techniques are more effective and efficient for skull stripping, especially in large lesions or with significant anatomical abnormalities [ 8 , 9 ]. Such methods can robustly deal with variations in image contrast and noise, but may require enormous amounts of training data and therefore greater computational cost [ 10 ]. Machine learning (ML)-based AI models of skull stripping have evolved through Deep Learning Neural Networks (DLNN) such as Convolutional Neural Networks (CNN).…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning (ML)-based AI models of skull stripping have evolved through Deep Learning Neural Networks (DLNN) such as Convolutional Neural Networks (CNN). AI experts have utilized the cutting-edge technology of DLNN to train models for medical image processing using chest X-ray images for the detection of COVID-19 [ 10 ]. CNN has revolutionized the field of image processing based on the principle of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

Azam,

Tariq,

Shehzad

et al. 2023

Brain Sciences

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This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask–RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet–101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask–RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).

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Contemporary Study on Deep Neural Networks to Diagnose COVID-19 Using Digital Posteroanterior X-ray Images

Cited by 4 publications

References 23 publications

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

A Lightweight CNN and Class Weight Balancing on Chest X-ray Images for COVID-19 Detection

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

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