Automated detection of Covid-19 disease using deep fused features from chest radiography images

Uçar, Emine; Atila, Ümit; Uçar, Murat; Akyol, Kemal

doi:10.1016/j.bspc.2021.102862

Cited by 19 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…www.ijacsa.thesai.org In particular, four pre-trained CNN model were evaluated for feature extraction, which are Efficient Net (EFFNET) [23], RESNET152 [24], NASNetMobile [25] and MobileNetV2 [26]. EFFNET has been adopted and demonstrated to have an outstanding performance in recent studies such as in the Covid-19 detection based on chest X-Ray [9], [27], smoke detection [8], fake video detection [12], pneumonia classification [10], masked face detection [7] and food recognition [28]. Meanwhile, the RESNET152 was also reported to have a good performance for scene recognition [1].…”

Section: ) Data Acquisitionmentioning

confidence: 99%

“…Although the classification accuracy obtained was good (92.17% and 94.4%), ResNet50 produced larger features dimensionality. Therefore, there are lot of studies in the other domain have various of Efficient Net (EFFNET) CNN architectures such as masked face recognition [7], smoke detection [8], chest X-ray scanning [9]- [11] and fake face video detection [12] due to its exceptional classification performance as well as to generate lightweight features.…”

Section: Introductionmentioning

confidence: 99%

“…It is also worth to note that many previous studies were adopted variant of EfficientNet (EFFNET) CNN architectures for extracting the features from the X-ray to detect lung related diseases. A variant of EFFNET namely EFFNETB0 with Bi-LSTM was proposed by [9] detect Covid-19 faster and with high accuracy low cost on chest X-ray images. Along with that, the features from EFFNETB0 were fused with DenseNet121 and LAB and CIE color space.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

Razali¹,

Tony²,

Ibrahim³

et al. 2023

IJACSA

View full text Add to dashboard Cite

Scene recognition algorithm is crucial for landmark recognition model development. Landmark recognition model is one of the main modules in the intelligent tour guide system architecture for the use of smart tourism industry. However, recognizing the tourist landmarks in the public places are challenging due to the common structure and the complexity of scene objects such as building, monuments and parks. Hence, this study proposes a super lightweight and robust landmark recognition model by using the combination of Convolutional Neural Network (CNN) and Linear Discriminant Analysis (LDA) approaches. The landmark recognition model was evaluated by using several pretrained CNN architectures for feature extraction. Then, several feature selections and machine learning algorithms were also evaluated to produce a super lightweight and robust landmark recognition model. The evaluations were performed on UMS landmark dataset and Scene-15 dataset. The results from the experiments have found that the Efficient Net (EFFNET) with CNN classifier are the best feature extraction and classifier. EFFNET-CNN achieved 100% and 94.26% classification accuracy on UMS-Scene and Scene-15 dataset respectively. Moreover, the feature dimensions created by EFFNet are more compact compared to the other features and even have significantly reduced for more than 90% by using Linear Discriminant Analysis (LDA) without jeopardizing classification performance but yet improved its performance.

show abstract

Section: ) Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

Razali¹,

Tony²,

Ibrahim³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Momeny et al improved the generalizability of deep CNN for the detection of COVID-19 with data augmentation and produced 78 percent of accuracy [ 19 ]. Ucar et al designed a system for automated detection of Covid-19 disease using deep fused features from chest radiography images with 92 percent of accuracy [ 20 ]. Frid-Adar et al developed a model that simultaneously detects COVID-19 from both CXR and CT images [ 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A deep learning-based COVID-19 classification from chest X-ray image: case study

Appasami

Nickolas

2022

Eur. Phys. J. Spec. Top.

View full text Add to dashboard Cite

The novel corona virus disease (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and more than 6 millions of people died in last 2 years. Early detection of COVID-19 can mitigate and control its spread. Reverse transcription polymerase chain reaction (RT-CPR), Chest X-ray (CXR) scan, and Computerized Tomography (CT) scan are used to identify the COVID-19. Chest X-ray image analysis is relatively time efficient than compared with RT-CPR and CT scan. Its cost-effectiveness make it a good choice for COVID-19 Classification. We propose a deep learning based Convolutional Neural Network model for detection of COVID-19 from CXR. Chest X-ray images are collected from various sources dataset for training with augmentation and evaluating our model, which is widely used for COVID-19 detection and diagnosis. A Deep Convolutional neural network (CNN) based model for analysis of COVID-19 with data augmentation is proposed, which uses the patient’s chest X-ray images for the diagnosis of COVID-19 with an aim to help the physicians to assist the diagnostic process among high workload conditions. The overall accuracy of 93 percent for COVID-19 Classification is achieved by choosing best optimizer.

show abstract

“…Uçar et al developed a model in which deep features were extracted in RGB, CIE Lab and RGB CIE color spaces and classified in two stages with various classifiers through pre-trained DL architectures for detecting COVID-19 from chest X-ray images. They stated that the Bi-LSTM network outperformed other classifiers with 92.489% in the experiments [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of COVID-19 from computed tomography images using modified U-Net model-based majority voting approach

Uçar

2022

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

The coronavirus disease (COVID-19) is an important public health problem that has spread rapidly around the world and has caused the death of millions of people. Therefore, studies to determine the factors affecting the disease, to perform preventive actions and to find an effective treatment are at the forefront. In this study, a deep learning and segmentation-based approach is proposed for the detection of COVID-19 disease from computed tomography images. The proposed model was created by modifying the encoder part of the U-Net segmentation model. In the encoder part, VGG16, ResNet101, DenseNet121, InceptionV3 and EfficientNetB5 deep learning models were used, respectively. Then, the results obtained with each modified U-Net model were combined with the majority vote principle and a final result was reached. As a result of the experimental tests, the proposed model obtained 85.03% Dice score, 89.13% sensitivity and 99.38% specificity on the COVID-19 segmentation test dataset. The results obtained in the study show that the proposed model will especially benefit clinicians in terms of time and cost.

show abstract

Automated detection of Covid-19 disease using deep fused features from chest radiography images

Cited by 19 publications

References 37 publications

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

A deep learning-based COVID-19 classification from chest X-ray image: case study

Automatic segmentation of COVID-19 from computed tomography images using modified U-Net model-based majority voting approach

Contact Info

Product

Resources

About