COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

Reis, Hatice Çatal; Turk, Veysel

doi:10.1016/j.artmed.2022.102427

Cited by 24 publications

(8 citation statements)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They found only 485 clinical notes available for 535 images. Seven of them used only Kaggle dataset to collect their data for training or testing purpose of developed model [35,38,42,44,46,49,51]. Three of them used only GitHub repository which was developed by Dr. Joseph Cohen [33,43,48], other four of them used both of the Kaggle and GitHub database [34,47,50,54].…”

Section: Exploration Of Used Datamentioning

confidence: 99%

“…Article [36,46] worked on only Hybrid model, Where [47] worked on both of the individual and hybrid model of chest X-ray and Computer Tomography scan. In Hybrid model, CT scan and X-ray image is merged in a single Image and processed.…”

Section: Exploration Of Used Datamentioning

confidence: 99%

“…Data Source Data Volume [42] Kaggle 2482 CT scans image and 31 Covid positive along with 10,192 normal X-ray image [48] GitHub 108,948 images [49] Kaggle 3829 X-rays and 3829 X-rays [43] GitHub 535 CT and X-ray images and 485 clinical notes related with them [37] Mendeley Data 17,599 images [44] Kaggle 2481 images [46] Kaggle 2357 CT scan data, 2515 chest X-ray data and 2400 CT and chest X-ray hybrid data [34] Kaggle, GitHub 5856 chest X-ray & CT dataset [54] Kaggle, GitHub 2905 unique images for X-ray 617,775 images from 4154 patients [41] Mendeley Data 8,055 CT scan and 9,544 X-ray images [51] Kaggle 2591 mixed data [33] GitHub 17100 X−ray and CT images [47] Kaggle, GitHub 168 Covid and 168 normal cases for the both Xray and CT scan images [36] GitHub Kaggle and Git Hub 723 X-ray and 3228 CT scans images [38] Kaggle 400 chest X-ray images, and 400 CT scan images [39] Wonkwang University Hospital(WKUH) and Chonnam National University Hospital (CNUH), Italian Society of Medical and Interventional Radiology(SIRM) public database.…”

Section: Literaturementioning

confidence: 99%

“…Sensitivity values of 100 % for typical pneumonia, 96.30 % for common pneumonia, and 96.58 % for COVID-19 are collected. [46]. The table 3 will show the list of various algorithm and there respected obtained accuracy, used or developed in the selected research articles.…”

Section: Exploring the Ai Techniquesmentioning

confidence: 99%

“…VGG-19 model shows an accuracy of 93% from CT scan and 94.17% from the chest X-ray data. Kathamuthu N.D. et al[46], applied Densenet121, VGG16 and 19, Incep-tionV3, Xception, and Resnet50 models to detect Covid and they found VGG16 model performed much better than other models with the accuracy of 98.00%. In another article Baz M. et al[17] achieved 99% accuracy by applying a Covid-19 detection tool which works by CNN model.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

Majhi¹,

Sauseng²

2023

Preprint

View full text Add to dashboard Cite

(1) Background: In the year of 2020 Covid-19 was declared epidemic by WHO. From that time millions of people were affected and died by this disease. The main detection process for this epidemic is RT-PCR test or reverse polymerase transcription chain reaction test. One of the reason of spreading of this disease so much is lack of efficiency in RT-PCR test. Sampling error and low viral load were two main reasons for what the testing process faced such problems. Lung infection is a very common symptom for covid-19 patients, so, CT scan and chest X-ray imaging technique can be applied to detect patient at a very early stage of infection. Which will be very effective and also can be a better option to RT-PCR test; (2) Methods: We searched data in Scopus for articles published between 2020 and 2023. The initial set of articles was 189, from which 21 articles will eventually be selected by exclusion criteria; (3) Results: A total of thirteen (61.90%) articles were found working on detecting Covid-19 by extracting the X-ray and CT scan data individually. Three (14.28%) of those articles focused on a hybrid model of CT scan and X-ray Image Data. Another four articles made a comparison between Covid-19, pneumonia and a normal person to identify a Covid-19 patient. Where others have worked on unsupervised learning methods or SVM to detect Covid-19.; (4) Conclusions: We have conducted a systematic review of the studies that have been published up to this time, with the purpose to present a summary of the evidence about the detection of COVID-19. In this article, we have summarized and critically reviewed literatures which worked on development or application or both of different AI or ML technique from chest CT and X-ray images to find a solution to detect covid-19.

show abstract

Section: Exploration Of Used Datamentioning

confidence: 99%

Section: Exploration Of Used Datamentioning

confidence: 99%

Section: Literaturementioning

confidence: 99%

Section: Exploring the Ai Techniquesmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

Majhi¹,

Sauseng²

2023

Preprint

View full text Add to dashboard Cite

show abstract

Adapting transfer learning models to dataset through pruning and Avg-TopK pooling

OZDEMIR

2024

Neural Comput & Applic

View full text Add to dashboard Cite

This study focuses on efficiently adapting transfer learning models to address the challenges of creating customized deep learning models for specific datasets. Designing a model from scratch can be time-consuming and complex due to factors like model complexity, size, and dataset structure. To overcome these obstacles, a novel approach is proposed using transfer learning models. The proposed method involves identifying relevant layers in transfer learning models and removing unnecessary ones using a layer-based variance pruning technique. This results in the creation of new models with improved computational efficiency and classification performance. By streamlining the models through layer-based variance pruning, the study achieves enhanced accuracy and faster computation. Experiments were conducted using the COVID-19 dataset and well-known transfer learning models, including InceptionV3, ResNet50V2, DenseNet201, VGG16, and Xception to validate the approach. Among these models, the variance-based layer pruning technique was applied to InceptionV3 and DenseNet201, yielding the best results. When these pruned models were combined with the new pooling layer, Avg-TopK, the proposed method achieved an outstanding image classification accuracy of 99.3%. Comparisons with previous models and literature studies indicate that the proposed approach outperforms existing methods, showcasing state-of-the-art performance. This high-performance approach provides great potential for diagnosing COVID-19 and monitoring disease progression, especially on hardware-limited devices. By leveraging transfer learning models, pruning, and efficient pooling techniques, the study presents a promising strategy for tackling challenges in custom model design, leading to exceptional results in such as image classification and segmentation tasks. The proposed methodology holds the potential to yield exceptional outcomes across a spectrum of tasks, encompassing disciplines such as image classification and segmentation.

show abstract

Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm

Yaqoob,

Verma,

Aziz

2024

J Med Syst

View full text Add to dashboard Cite

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

Cited by 24 publications

References 97 publications

Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

Adapting transfer learning models to dataset through pruning and Avg-TopK pooling

Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm

Contact Info

Product

Resources

About