The novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use fast and inexpensive images from computed tomography (CT) scans. In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), convolutional auto-encoder neural network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19. The dataset consists of 1252 CT scans that are positive and 1230 CT scans that are negative for COVID-19 virus. The proposed models have priority over the other models that there is no need of pre-trained networks and data augmentation for them. The classification accuracies of ResNet-50, VGG-16, CNN, and CAENN were obtained 92.24%, 94.07%, 93.84%, and 93.04% respectively. Among ML classifiers, the nearest neighbor (NN) had the highest performance with an accuracy of 94%.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205564 | PMC |
| http://dx.doi.org/10.1016/j.comcom.2021.06.011 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Improved Osteoporosis Prediction in Breast Cancer Patients Using a Novel Semi-Foundational Model.
J Imaging Inform Med
December 2024
Department of Radiology, USF Health, Tampa, USA.
Small cohorts of certain disease states are common especially in medical imaging. Despite the growing culture of data sharing, information safety often precludes open sharing of these datasets for creating generalizable machine learning models. To overcome this barrier and maintain proper health information protection, foundational models are rapidly evolving to provide deep learning solutions that have been pretrained on the native feature spaces of the data.
View Article and Find Full Text PDFAdvanced Segmentation of Gastrointestinal (GI) Cancer Disease Using a Novel U-MaskNet Model.
Life (Basel)
November 2024
Department of Electrical, Computer Engineering and Computer Science, Ohio Northern University, Ada, OH 45810, USA.
The purpose of this research is to contribute to the development of approaches for the classification and segmentation of various gastrointestinal (GI) cancer diseases, such as dyed lifted polyps, dyed resection margins, esophagitis, normal cecum, normal pylorus, normal Z line, polyps, and ulcerative colitis. This research is relevant and essential because of the current challenges related to the absence of efficient diagnostic tools for early diagnostics of GI cancers, which are fundamental for improving the diagnosis of these common diseases. To address the above challenges, we propose a new hybrid segmentation model, U-MaskNet, which is a combination of U-Net and Mask R-CNN models.
View Article and Find Full Text PDFDetection of Subarachnoid Hemorrhage Using CNN with Dynamic Factor and Wandering Strategy-Based Feature Selection.
Diagnostics (Basel)
October 2024
Laboratory of Heat-Equipment Research and Testing, Lithuanian Energy Institute, Breslaujos st. 3, 44403 Kaunas, Lithuania.
: Subarachnoid Hemorrhage (SAH) is a serious neurological emergency case with a higher mortality rate. An automatic SAH detection is needed to expedite and improve identification, aiding timely and efficient treatment pathways. The existence of noisy and dissimilar anatomical structures in NCCT images, limited availability of labeled SAH data, and ineffective training causes the issues of irrelevant features, overfitting, and vanishing gradient issues that make SAH detection a challenging task.
View Article and Find Full Text PDFPrediction of lumpy skin disease virus using customized CBAM-DenseNet-attention model.
BMC Infect Dis
October 2024
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
Article Synopsis
- Lumpy skin disease virus (LSDV) is a highly contagious viral disease primarily affecting cows, spread mainly by mosquitoes and ticks, with outbreaks notably occurring in Pakistan, India, and Iran.
- The lack of extensive publicly available datasets has complicated the early detection and classification of LSDV, prompting researchers to gather images from various online sources and veterinary farms in Pakistan.
- Utilizing advanced deep learning models, particularly DenseNet, this study achieved high accuracy rates of 99.11% on augmented datasets and 94.23% on original datasets for detecting LSDV, chicken pox, and monkey pox, demonstrating the effectiveness of the proposed methods compared to existing studies.
Mineral prospectivity prediction based on convolutional neural network and ensemble learning.
Sci Rep
September 2024
School of Earth Science and Resources, Chang'an University, 710054, Xi'an, China.
Current research in deep learning, which is widely used in mineral prospectivity prediction, focuses on obtaining high-performance models to predict mineral resources. However, because the network structure and depth of different algorithms differ, there are some differences in the correlation between the spatial pattern of ore-generating geological big data and the spatial location of discovered ore deposits; this causes instability in the prediction. To solve this problem, this paper proposes the use of ensemble learning to synthesize convolutional neural network algorithms and self-attention mechanism algorithms for mineral prospectivity prediction.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!