A novel machine learning‐based analytical framework for automatic detection of <scp>COVID</scp>‐19 using chest <scp>X‐ray</scp> images

Johri, Shikhar; Goyal, Mehendi; Jain, Sahil; Baranwal, Manoj; Kumar, Vinay; Upadhyay, Rahul

doi:10.1002/ima.22613

Cited by 23 publications

(10 citation statements)

References 40 publications

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“…From the above Table 8 , it is evident that our proposed model has higher efficiency of 8–11% when compared to the performance of existing models. The framework mentioned in the paper [ 31 ] was designed, trained, and validated to identify four classes of CXR images, namely healthy, bacterial pneumonia, viral pneumonia, and COVID-19. The experimental results pose the proposed framework as a potential candidate for COVID-19 disease diagnosis using CXR images, with training, validation, and testing accuracy of 92.4%, 88.24%, and 87.13%, respectively, in four-class classification.…”

Section: Resultsmentioning

confidence: 99%

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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Section: Resultsmentioning

confidence: 99%

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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“…In their study, various deep networks are considered as the backbone. A machine learning-based analytical framework also demonstrated better results in COVID-19 detection [19]. A deep CNN model with chest X-ray images taken from portable devices is used to effectively detect the COVID-19 [20].…”

Section: Related Workmentioning

confidence: 99%

“…In 2019, a novel coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) emerged from Wuhan, China and infected more than 229 million people with a mortality of over 4.7 million 1 . The SARS-CoV-2 cause respiratory disease, namely coronavirus disease 19 (COVID- 19), which is the key reason for the current COVID-19 pandemic [1]. This virus can spread from one person to another primarily through the droplets of an infected person.…”

Section: Introductionmentioning

confidence: 99%

Deep COVID-19 Recognition Using Chest X-ray Images: A Comparative Analysis

Thuseethan

Wimalasooriya

Vasanthapriyan

2021

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)

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The novel coronavirus variant, which is also widely known as COVID-19, is currently a common threat to all humans across the world. Effective recognition of COVID-19 using advanced machine learning methods is a timely need. Although many sophisticated approaches have been proposed in the recent past, they still struggle to achieve expected performances in recognizing COVID-19 using chest X-ray images. In addition, the majority of them are involved with the complex pre-processing task, which is often challenging and time-consuming. Meanwhile, deep networks are end-to-end and have shown promising results in image-based recognition tasks during the last decade. Hence, in this work, some widely used state-of-the-art deep networks are evaluated for COVID-19 recognition with chest X-ray images. All the deep networks are evaluated on a publicly available chest X-ray image datasets. The evaluation results show that the deep networks can effectively recognize COVID-19 from chest X-ray images. Further, the comparison results reveal that the EfficientNetB7 network outperformed other existing state-of-theart techniques.

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“…It is not correct and should be differentiated between the two terms. In fact, artificial intelligence includes a learning spectrum and is not limited to machine learning [3,4]. AI includes representation learning, deep learning, and natural language processing (NLP).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

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A novel machine learning‐based analytical framework for automatic detection of COVID‐19 using chest X‐ray images

Cited by 23 publications

References 40 publications

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

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

Deep COVID-19 Recognition Using Chest X-ray Images: A Comparative Analysis

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

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