Viral and Bacterial Pneumonia Detection using Artificial Intelligence in the Era of COVID-19

Özsöz, Mehmet; Ibrahim, Abdullahi Umar; Serte, Sertan; Al‐Turjman, Fadi; Yakoi, Polycarp Shizawaliyi

doi:10.21203/rs.3.rs-70158/v1

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…This application may be utilized with any device and by all types of the medical personnel for detecting of COVID patients by the utilization of the CXR images in a matter of seconds. A DL technique based upon the Pre-trained AlexNet Model was suggested by Ozsoz et multiclass datasets, the model could achieve an accuracy of 94% [57]. Table 1 summarizes diagnosis based upon CXR images.…”

Section: Diagnosis Based On Cxr Imagesmentioning

confidence: 99%

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Saeed¹,

Oleiwi²

2022

ISI

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The outbreak of the new coronavirus (COVID-19) has created a disaster worldwide and it became a very severe and acute disease. COVID-19 prevalence is rapidly increasing around the world. Deep learning (DL) technology had become a hot topic in the context of computing and is widely applied in various medical applications. These techniques have proven to be one of the effective tools for clinicians in the automatic diagnoses of COVID-19. The goal of the present paper is to provide an overview of recently developed systems based on DL techniques that use various medical imaging modalities such as Computer Tomography (CT) and Chest X-Rays (CXR). This review focuses on systems that had been developed for the diagnosis of COVID-19 with the use of the DL methods, as well as the well-known datasets that are utilized for the training of those networks. Finally, the researcher reviewed 58 research papers based on different medical images. Overall, this article aims to assist experts (medical or otherwise) and technicians to understand how the DL approaches are utilized in this context and the way that they can potentially be expanded to combat COVID-19 outbreaks.

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Section: Diagnosis Based On Cxr Imagesmentioning

confidence: 99%

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Saeed¹,

Oleiwi²

2022

ISI

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“…Chest X-rays provide a limited view of the lungs and are less sensitive than other imaging modalities, such as computed tomography (CT) scans, in detecting specific lung abnormalities. This limitation can make it difficult to differentiate between COVID-19 and bacterial pneumonia based solely on chest X-ray findings [5][6][7][8][9][10]. The studies focused on improving the detection of COVID-19 using chest X-ray images, aiming to minimize false results and support medical professionals in early diagnosis [11,12].…”

Section: Introductionmentioning

confidence: 99%

Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography

Lin

Okamoto

et al. 2023

Healthcare

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Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.

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Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques

Venkataramana¹,

Prasad²,

Saraswathi³

et al. 2022

Med Biol Eng Comput

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Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy. Graphical Abstract

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Viral and Bacterial Pneumonia Detection using Artificial Intelligence in the Era of COVID-19

Cited by 6 publications

References 25 publications

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography

Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques

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