Automated COVID-19 Detection from CT Images Using Deep Learning

Subaşı, Abdülhamit; Mitra, Arka; Özyurt, Fatih; Tuncer, Türker

doi:10.1201/9781003121152-7

Cited by 12 publications

(7 citation statements)

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“…ResNet was created due to a problem researchers encountered when adding layers to the CNN architecture. As the layers are added to the CNN architecture, the performance increases to a point but decreases after a point 35 . It is seen that gradient calculation needs to be performed in the ResNet model developed to solve this problem.…”

Section: Methodsmentioning

confidence: 99%

“…As the layers are added to the CNN architecture, the performance increases to a point but decreases after a point. 35 It is seen that gradient calculation needs to be performed in the ResNet model developed to solve this problem. Instead, a shortcut link is provided by arithmetically adding x to the f(x) function.…”

Section: Resnet Architecturesmentioning

confidence: 99%

“…The ResNet-50 model consists of convolutional block arrays with mean pooling. 35 Softmax is used as the last layer.…”

Section: Resnet Architecturesmentioning

confidence: 99%

See 2 more Smart Citations

Breast lesion classification using features fusion and selection of ensemble ResNet method

Kilicarslan¹,

Koç

Özyurt

et al. 2023

Int J Imaging Syst Tech

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Medical Imaging with Deep Learning has recently become the most prominent topic in the scientific world. Significant results have been obtained in the classification of medical images using deep learning methods, and there has been an increase in studies on malignant types. The main reason for choosing breast cancer is that breast cancer is one of the critical malignant types that increase the death rate in women. In this study, 1236 ultrasound images were collected from Elazig Fethi Sekin City Hospital, and three different ResNet CNN architectures were used for feature extraction. Data were trained with an SVM classifier. In addition, the three ResNet architectures were combined, and novel fused ResNet architecture was used in this study. In addition, these features were used with three different feature selection techniques, MR‐MR, NCA, and Relieff. These results are 89.3% obtained from ALL‐ResNet architecture and the feature selected with NCA in normal and lesion classification. Normal, malignant, and benign classification best accuracy is 84.9% with ALL‐ResNet NCA. Experimental studies show that MR‐MR, NCA, and Relieff feature selection algorithms reduce features and give more results that are successful. This indicates that the proposed method is more successful than classical deep learning methods.

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Section: Methodsmentioning

confidence: 99%

Section: Resnet Architecturesmentioning

confidence: 99%

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Breast lesion classification using features fusion and selection of ensemble ResNet method

Kilicarslan¹,

Koç

Özyurt

et al. 2023

Int J Imaging Syst Tech

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“…In [37], the authors proposed utilizing different DL models to extract features from the input CT scan image and then several ML model were utilized to classify the input CT scan image based on the extracted feature with the DL model. The limitations of this work are three-fold.…”

Section: Plos Onementioning

confidence: 99%

A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans

Akl¹,

Hosny

Fouda

et al. 2023

PLoS ONE

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COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

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“…Deep learning is a machine learning technology that has developed rapidly in recent years. It has become the essential tool for data processing in computer vision work and has been widely applied in many fields such as agricultural product classification 10 , synthetic speech recognition 11 , animal behavior analysis 12 , sensor signal recognition 13 and COVID-19 detection 14 , 15 . The convolutional neural network (CNN) is a feedforward neural network that includes convolutional computation and has a deep structure, it is one of the main architectures of deep learning 16 , 17 .…”

Section: Introductionmentioning

confidence: 99%

A novel method for peanut variety identification and classification by Improved VGG16

Yang

Gao

et al. 2021

Sci Rep

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Crop variety identification is an essential link in seed detection, phenotype collection and scientific breeding. This paper takes peanut as an example to explore a new method for crop variety identification. Peanut is a crucial oil crop and cash crop. The yield and quality of different peanut varieties are different, so it is necessary to identify and classify different peanut varieties. The traditional image processing method of peanut variety identification needs to extract many features, which has defects such as intense subjectivity and insufficient generalization ability. Based on the deep learning technology, this paper improved the deep convolutional neural network VGG16 and applied the improved VGG16 to the identification and classification task of 12 varieties of peanuts. Firstly, the peanut pod images of 12 varieties obtained by the scanner were preprocessed with gray-scale, binarization, and ROI extraction to form a peanut pod data set with a total of 3365 images of 12 varieties. A series of improvements have been made to VGG16. Remove the F6 and F7 fully connected layers of VGG16. Add Conv6 and Global Average Pooling Layer. The three convolutional layers of conv5 have changed into Depth Concatenation and add the Batch Normalization(BN) layers to the model. Besides, fine-tuning is carried out based on the improved VGG16. We adjusted the location of the BN layers. Adjust the number of filters for Conv6. Finally, the improved VGG16 model's training test results were compared with the other classic models, AlexNet, VGG16, GoogLeNet, ResNet18, ResNet50, SqueezeNet, DenseNet201 and MobileNetv2 verify its superiority. The average accuracy of the improved VGG16 model on the peanut pods test set was 96.7%, which was 8.9% higher than that of VGG16, and 1.6–12.3% higher than that of other classical models. Besides, supplementary experiments were carried out to prove the robustness and generality of the improved VGG16. The improved VGG16 was applied to the identification and classification of seven corn grain varieties with the same method and an average accuracy of 90.1% was achieved. The experimental results show that the improved VGG16 proposed in this paper can identify and classify peanut pods of different varieties, proving the feasibility of a convolutional neural network in variety identification and classification. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification.

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Automated COVID-19 Detection from CT Images Using Deep Learning

Cited by 12 publications

References 0 publications

Breast lesion classification using features fusion and selection of ensemble ResNet method

Breast lesion classification using features fusion and selection of ensemble ResNet method

A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans

A novel method for peanut variety identification and classification by Improved VGG16

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