IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

Le, Dac‐Nhuong; Velmurugan, S.; Gupta, Deepak; Khanna, Ashish; Rodrigues, Joel J. P. C.; Shankar, K.

doi:10.1007/s13042-020-01248-7

Cited by 117 publications

(59 citation statements)

References 25 publications

(16 reference statements)

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“…The authors claim that they achieved up to 95% accuracy using the Support Vector Machine. Dac Nhuong Le et al [43] proposed a novel IoT-enabled deep support vector machine (DSVM) and Depthwise separable convolution neural network (DWS-CNN) to classify COVID-19 disease. The DWS-CNN model detects both multiple and binary classes of COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

et al. 2022

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In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to diagnose the virus from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

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Section: Related Workmentioning

confidence: 99%

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

et al. 2022

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“…The COVID19 disease affects the human lungs, which can be identified by examining the lung X-ray. A successful CNN approach has been used with a convolutional neural network (CNN) to predict the Pneumonia case from chest X-ray [16][17][18][19][20]. The training process take place on a set of images offering the front view of lung X-ray images [21].…”

Section: Related Workmentioning

confidence: 99%

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

et al. 2021

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In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.

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“…Mori et al, (Mori et al, 2016), used SVM to predict the outbreak of disaster for a specific location. SVM was used with IoT (Internet of Things) and CNN (Convolutional Neural Networks) for the diagnosis of COVID-19 (Le et al, 2021).…”

Section: Classificationmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also

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IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

Cited by 117 publications

References 25 publications

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

Battling COVID-19 using machine learning: A review

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