Convolutional neural network approach for automatic tympanic membrane detection and classification

Başaran, Erdal; Cömert, Zafer; Çelik, Yüksel

doi:10.1016/j.bspc.2019.101734

Cited by 63 publications

(59 citation statements)

References 28 publications

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“…The output feature map is obtained in Equation (1) , where

shows the local features obtained from the previous layers, and

and

denote the adjustable kernels and training bias, respectively. Bias is used to prevent overfitting during the training of the CNN ( Başaran, Cömert, & Çelik, 2020 );

…”

Section: Methodsmentioning

confidence: 99%

Deep learning approaches for COVID-19 detection based on chest X-ray images

Ismael

Şengür

2021

Expert Systems with Applications

595

366

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“…The output feature map is obtained in Equation (1) , where

shows the local features obtained from the previous layers, and

and

denote the adjustable kernels and training bias, respectively. Bias is used to prevent overfitting during the training of the CNN ( Başaran, Cömert, & Çelik, 2020 );

…”

Section: Methodsmentioning

confidence: 99%

Deep learning approaches for COVID-19 detection based on chest X-ray images

Ismael

Şengür

2021

Expert Systems with Applications

595

366

View full text Add to dashboard Cite

show abstract

“…CNNs are architectures consisting of a large number of sequenced layers. Layers that perform different functions are used in these architectures to reveal the distinctive features of the data applied as input [24] . In general, the tasks of these layers can be summarized as follows:…”

Section: Methodsmentioning

confidence: 99%

“…Offline or online data augmentation techniques can be used to realize a more efficient training for the computational models [24] . However, it is essential to be aware that the data augmentation techniques should not be used on the test set because of the overfitting problem.…”

Section: Methodsmentioning

confidence: 99%

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Nour

Cömert

Polat

2020

Applied Soft Computing

Self Cite

276

215

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A pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases.

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“…Erdal Basaran (2020) proposed a diagnosis method that combined a rapid R-CNN and a pre-trained CNN. This method could be applied to future otology clinical decision support systems to improve the diagnostic accuracy of physicians and reduce the overall misdiagnosis rate [25]. J. Jin (2014) described the architecture of a TSR model and proposed to use the HLSGD method to train CNNs [26].…”

Section: A Citation Cluster Analysismentioning

confidence: 99%

Bibliometric Analysis of the Application of Convolutional Neural Network in Computer Vision

Chen

Deng

2020

IEEE Access

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This paper analyzes the research progress in field of Convolutional Neural Networks(CNNs) using the bibliometric method. Literature samples of CNNs are analyzed by a basic statistic and co-citation network. Experimental results show that CNNs are being utilized in many computer vision applications, such as fault and image recognition diagnosis, seismic detection, positioning, and automatic detection of cracks and signals, image classification and image segmentation. In addition, there is systematic research on unbalanced problems in CNNs. Quantitative experimental research, extensive application fields, and market research informatization will be the three vital research tendencies in the future. The ideas and conclusions of this paper provide insights to the academic research of CNNs and their practical application in the corporate world.

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Convolutional neural network approach for automatic tympanic membrane detection and classification

Cited by 63 publications

References 28 publications

Deep learning approaches for COVID-19 detection based on chest X-ray images

Deep learning approaches for COVID-19 detection based on chest X-ray images

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Bibliometric Analysis of the Application of Convolutional Neural Network in Computer Vision

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