Diagnosis of COVID-19 using CT scan images and deep learning techniques

Shah, Vruddhi; Keniya, Rinkal; Shridharani, Akanksha; Punjabi, Manav; Shah, Jainam; Mehendale, Ninad

doi:10.1007/s10140-020-01886-y

Cited by 241 publications

(154 citation statements)

References 25 publications

Supporting

Mentioning

154

Contrasting

Order By: Relevance

“…The VGG deep architecture achieves high classification accuracy rates despite its huge memory requirements. In [43], the authors utilized a dataset of 592 CT scan images with two classes COVID-19 and normal. Then, they proposed the CTnet-10 model, a binary classifier model.…”

Section: Plos Onementioning

confidence: 99%

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi

Hosny

Darwish

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.

show abstract

Section: Plos Onementioning

confidence: 99%

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi

Hosny

Darwish

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Since the deep learning approach has proved its efficiency in most computer vision tasks [11] (including medical imaging tasks [3,12]), most of the approaches that have been proposed for COVID-19 analysis from CT scans are CNN-based approaches. In general, the approaches that use CT scans for COVID-19 analysis can be classified into: segmentation [13][14][15] and recognition [16][17][18][19][20] approaches.…”

Section: Related Workmentioning

confidence: 99%

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Bougourzi

Contino

Distante

et al. 2021

Sensors

View full text Add to dashboard Cite

Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.

show abstract

“…The ML, in fact, could become a helpful and potential powerful tool for large-scale COVID-19 screening [13,65], and the author in Reference [3] has recently hypothesized that DL techniques applied to CT scans can become the first alternative screening test to the rRT-PCR in the near future. Motivated by this expectation, in the last year, the DL has been successfully used for CXRs [15,20,66,67], CT scans [56,[68][69][70], or both [18,71]. Being, indeed, challenging to summarize all the available literature in a single paper, there are some useful reviews regarding the application of DL techniques to COVID-19 detection on CXRs [72], CT scans [73,74], and both [65,75].…”

Section: Related Workmentioning

confidence: 99%

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

et al. 2021

View full text Add to dashboard Cite

The global COVID-19 pandemic certainly has posed one of the more difficult challenges for researchers in the current century. The development of an automatic diagnostic tool, able to detect the disease in its early stage, could undoubtedly offer a great advantage to the battle against the pandemic. In this regard, most of the research efforts have been focused on the application of Deep Learning (DL) techniques to chest images, including traditional chest X-rays (CXRs) and Computed Tomography (CT) scans. Although these approaches have demonstrated their effectiveness in detecting the COVID-19 disease, they are of huge computational complexity and require large datasets for training. In addition, there may not exist a large amount of COVID-19 CXRs and CT scans available to researchers. To this end, in this paper, we propose an approach based on the evaluation of the histogram from a common class of images that is considered as the target. A suitable inter-histogram distance measures how this target histogram is far from the histogram evaluated on a test image: if this distance is greater than a threshold, the test image is labeled as anomaly, i.e., the scan belongs to a patient affected by COVID-19 disease. Extensive experimental results and comparisons with some benchmark state-of-the-art methods support the effectiveness of the developed approach, as well as demonstrate that, at least when the images of the considered datasets are homogeneous enough (i.e., a few outliers are present), it is not really needed to resort to complex-to-implement DL techniques, in order to attain an effective detection of the COVID-19 disease. Despite the simplicity of the proposed approach, all the considered metrics (i.e., accuracy, precision, recall, and F-measure) attain a value of 1.0 under the selected datasets, a result comparable to the corresponding state-of-the-art DNN approaches, but with a remarkable computational simplicity.

show abstract

Diagnosis of COVID-19 using CT scan images and deep learning techniques

Cited by 241 publications

References 25 publications

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

Contact Info

Product

Resources

About