BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model

Toğaçar, Mesut; Ergen, Burhan; Cömert, Zafer

doi:10.1016/j.mehy.2019.109531

Cited by 207 publications

(75 citation statements)

References 35 publications

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“…The performance of each model was evaluated based on different metrics like F1-score, Specificity (Spe), Sensitivity (Sen), Precision (Pre), Accuracy (Acc), and Area Under the ROC Curve (AUC). These metrics were computed by different parameters of the confusion matrix such as True Positive (TP), False Positive (FP), True Negative (TN) and False Negative (FN) [41] , [42] . The metrics are defined as follows:…”

Section: Experiments and Resultsmentioning

confidence: 99%

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Nayak

Sinha

et al. 2021

Biomedical Signal Processing and Control

337

193

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Section: Experiments and Resultsmentioning

confidence: 99%

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Nayak

Sinha

et al. 2021

Biomedical Signal Processing and Control

337

193

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“… Convolution layer: This layer is the main building block of CNN architectures, and it is used to reveal the discriminative features of the input data. This layer applies some filter families to the data so as to reveal low and high-level features in the data [25] . After the convolution process, the size of the input data changes.…”

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

280

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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“…Pooling layers are also used in the MobileNetV2 model. The matrices obtained through these layers are converted into smaller dimensions [19].…”

Section: Deep Learning Model: Mobilenetv2mentioning

confidence: 99%

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Toğaçar

Ergen

Cömert

2020

Computers in Biology and Medicine

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498

360

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Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.

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BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model

Cited by 207 publications

References 35 publications

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

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

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

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