A novel DeepNet model for the efficient detection of COVID-19 for symptomatic patients

Panthakkan, Alavikunhu; Anzar, S. M.; Mansoori, Saeed Al; Al-Ahmad, Hussain

doi:10.1016/j.bspc.2021.102812

Cited by 13 publications

(7 citation statements)

References 36 publications

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“…Experiments were performed on two public hospital datasets and the recognition accuracy rate of 99% was reported. In 2021, Panthakkan et al [27] provided a highly efficient and novel method named COVID-DeepNet for rapid and accurate detection of COVID-19 which was reported to work as a multi-class classification of X-ray images into normal (healthy), non-COVID Pneumonia, and COVID-19. An accuracy rate of 99.67% was observed for the dataset size of 7500 images.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

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A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging

2022

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The world has witnessed dramatic changes because of the advent of COVID19 in the last few days of 2019. During the last more than two years, COVID-19 has badly affected the world in diverse ways. It has not only affected human health and mortality rate but also the economic condition on a global scale. There is an urgent need today to cope with this pandemic and its diverse effects. Medical imaging has revolutionized the treatment of various diseases during the last four decades. Automated detection and classification systems have proven to be of great assistance to the doctors and scientific community for the treatment of various diseases. In this paper, a novel framework for an efficient COVID-19 classification system is proposed which uses the hybrid feature extraction approach. After preprocessing image data, two types of features i.e., deep learning and handcrafted, are extracted. For Deep learning features, two pre-trained models namely ResNet101 and DenseNet201 are used. Handcrafted features are extracted using Weber Local Descriptor (WLD). The Excitation component of WLD is utilized and features are reduced using DCT. Features are extracted from both models, handcrafted features are fused, and significant features are selected using entropy. Experiments have proven the effectiveness of the proposed model. A comprehensive set of experiments have been performed and results are compared with the existing well-known methods. The proposed technique has performed better in terms of accuracy and time.

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Section: Deep Learning Methodsmentioning

confidence: 99%

“…The accuracy of COVID-19 detection mainly depends on the type of features extracted from images. The feature extraction methods of COVID-19 fall into these categories: Traditional handcrafted (HC) features [22][23][24][25] and deep learning methods [26][27][28][29]. Here we present the state of research related to these methods.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging

2022

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“…The LeNet-5, a seven-layer CNN, is the cornerstone of the current CNN design. The fundamentals of the proposed model are explored in [9]. These methods have proven successful in clinical diagnosis of a wide range of diseases.…”

Section: Methodsmentioning

confidence: 99%

“…According to recent studies, Deep Learning algorithms have been successfully deployed in a number of clinical applications, including breast cancer detection, brain disease classification, diabetic retinopathy, fundus image segmentation, cardiac arrhythmia detection, pneumonia, lung segmentation and skin cancer classification [9]. Deep learning is a type of machine learning that focuses on learning from enormous amounts of information and enables the creation of a powerful end-to-end model without the use of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

A systematic comparison of transfer learning models for COVID-19 prediction

Panthakkan

Anzar²,

Mansoori³

et al. 2022

IDT

Self Cite

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The pandemic COVID-19 is already in its third year and there is no sign of ebbing. The world continues to be in a never-ending cycle of disease outbreaks. Since the introduction of Omicron-the most mutated and transmissible of the five variants of COVID-19 – fear and instability have grown. Many papers have been written on this topic, as early detection of COVID-19 infection is crucial. Most studies have used X-rays and CT images as these are highly sensitive to detect early lung changes. However, for privacy reasons, large databases of these images are not publicly available, making it difficult to obtain very accurate AI Deep Learning models. To address this shortcoming, transfer learning (pre-trained) models are used. The current study aims to provide a thorough comparison of known AI Deep Transfer Learning models for classifying lung radiographs into COVID-19, non COVID pneumonia and normal (healthy). The VGG-19, Inception-ResNet, EfficientNet-B0, ResNet-50, Xception and Inception models were trained and tested on 3568 radiographs. The performance of the models was evaluated using accuracy, sensitivity, precision and F1 score. High detection accuracy scores of 98% and 97% were found for the VGG-19 and Inception-ResNet models, respectively.

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“…CNN architectures have also been utilized for the diagnosis of COVID-19. [4] and [5] provided lists of previous studies where different CNNs models were applied to detect COVID-19 infection. They compared the studies in terms of the dataset used, the CNN models or other techniques adopted, and the performance metrics achieved.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and Non-COVID-19 Classification from Lung CT-Scan Images Using Deep Convolutional Neural Networks

Dolma

2023

IJMSIT

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In this study, three different convolutional neural network (CNN) architectures have been used for SARS-COV-2 infection (COVID-19) detection from lung Computerized Tomography (CT) scan images. The dataset comprises 2481 lung CTscan images, of which 1252 are positive for COVID-19 infection. First, a simple CNN, LeNet-5, was trained from scratch, which resulted in poor classification performance with an accuracy value of 0.78. Then, to overcome the drawback of the limited availability of data, the convolutional bases of two pre-trained networks, VGG-16 and MobileNet, were leveraged to extract features from the dataset. On top of the feature extraction outputs, new classifiers were trained. When the VGG16 and the MobileNet CNN's convolutional bases were used for feature extraction, accuracy values of 0.974 and 0.984 were obtained, respectively. The findings indicate that using pre-trained CNN models for feature extraction and then training a simpler, fully connected network structure for classification successfully differentiates CT-scan images of patients with COVID-19 infection from the ones without COVID-19 infection.

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A novel DeepNet model for the efficient detection of COVID-19 for symptomatic patients

Cited by 13 publications

References 36 publications

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging

A systematic comparison of transfer learning models for COVID-19 prediction

COVID-19 and Non-COVID-19 Classification from Lung CT-Scan Images Using Deep Convolutional Neural Networks

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