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    A novel deep convolutional neural network model for COVID-19 disease detection
    (Institute of Electrical and Electronics Engineers Inc., 2020) Irmak, Emrah
    The novel coronavirus, generally known as COVID19, is a new type of coronavirus which first appeared in Wuhan Province of China in December 2019. The biggest impact of this new coronavirus is its very high contagious feature which brings the life to a halt. As soon as data about the nature of this dangerous virus are collected, the research on the diagnosis of COVID-19 has started to gain a lot of momentum. Today, the gold standard for COVID-19 disease diagnosis is typically based on swabs from the nose and throat, which is time-consuming and prone to manual errors. The sensitivity of these tests are not high enough for early detection. These disadvantages show how essential it is to perform a fully automated framework for COVID-19 disease diagnosis based on deep learning methods using widely available X-ray protocols. In this paper, a novel, powerful and robust Convolutional Neural Network (CNN) model is designed and proposed for the detection of COVID-19 disease using publicly available datasets. This model is used to decide whether a given chest X-ray image of a patient has COVID-19 or not with an accuracy of 99.20%. Experimental results on clinical datasets show the effectiveness of the proposed model. It is believed that study proposed in this research paper can be used in practice to help the physicians for diagnosing the COVID-19 disease. © 2020 IEEE.
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    A novel implementation of deep-learning approach on malaria parasite detection from thin blood cell images
    (2021) Irmak, Emrah
    Malaria is known as an acute febrile disease caused by the bite of female Anopheles mosquitoes, and it manifests itself with symptoms such as headache, fever, chills, vomiting, and fatigue. The diagnosis of malaria is still based on manual identification of Plasmodium parasitized cells in microscopic examinations of blood cells known as parasite based microscopy diagnostic testing. The accuracy of this manual diagnosis meth , is clearly affected by the level of microscopists experience, which makes this diagnosis method susceptible to manual error and time consuming. Diagnoses of diseases made using deep learning methods have had great repercussions in the medical world, especially in recent years; and this indicate, that the diagnosis of malaria can also be achieved by deep learning methods. On the basis of this fact, this paper presents a novel deep-learning-based malaria disease detection technique. A convolutional neural network (CNN) architecture, which has 20 weighted layers is designed and proposed to identify parasitized microscopic images from uninfected microscopic images. A total of 27,558 thin blood cell images were used to train and test the CNN model, and 95.28% overall accuracy was obtained. The experimental results on large clinical dataset show the effectiveness of the piposed deep learning method for malaria disease detection.
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    COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model
    (2022) Irmak, Emrah
    Clinical reports show that COVID-19 disease has impacts on the cardiovascular system in addition to the respiratory system. Available COVID-19 diagnostic methods have been shown to have limitations. In addition to current diagnostic methods such as low-sensitivity standard RT-PCR tests and expensive medical imaging devices, the development of alternative methods for the diagnosis of COVID-19 disease would be benefcial for control of the COVID-19 pandemic. Further, it is important to quickly and accurately detect abnormalities caused by COVID-19 on the cardiovascular system via ECG. In this study, the diagnosis of COVID-19 disease is proposed using a novel deep Convolutional Neural Network model by using only ECG trace images created from ECG signals of COVID-19 infected patients based on the abnormalities caused by the COVID-19 virus on the cardiovascular system. An overall classifcation accuracy of 98.57%, 93.20%, 96.74% and AUC value of 0.9966, 0.9771, 0.9905 is achieved for COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, COVID-19 vs. Myocardial Infarction binary classifcation tasks, respectively. In addition, an overall classifcation accuracy of 86.55% and 83.05% is achieved for COVID-19 vs. Abnormal Heartbeats vs. Myocardial Infarction and Normal vs. COVID-19 vs. Abnormal Heartbeats vs. Myocardial Infarction multi-classifcation tasks. This study is believed to have great potential to speed up the diagnosis and treatment of COVID-19 patients, saving clinicians time and facilitating the control of the pandemic.
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    COVID-19 disease severity assessment using CNN model
    (2021) Irmak, Emrah
    Due to the highly infectious nature of the novel coronavirus (COVID-19) disease, excessive number of patients waits in the line for chest X-ray examination, which overloads the clinicians and radiologists and negatively affects the patient's treatment, prognosis and control of the pandemic. Now that the clinical facilities such as the intensive care units and the mechanical ventilators are very limited in the face of this highly contagious disease, it becomes quite important to classify the patients according to their severity levels. This paper presents a novel implementation of convolutional neural network (CNN) approach for COVID-19 disease severity classification (assessment). An automated CNN model is designed and proposed to divide COVID-19 patients into four severity classes as mild, moderate, severe, and critical with an average accuracy of 95.52% using chest X-ray images as input. Experimental results on a sufficiently large number of chest X-ray images demonstrate the effectiveness of CNN model produced with the proposed framework. To the best of the author's knowledge, this is the first COVID-19 disease severity assessment study with four stages (mild vs. moderate vs. severe vs. critical) using a sufficiently large number of X-ray images dataset and CNN whose almost all hyper-parameters are automatically tuned by the grid search optimiser.
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    Implementation of convolutional neural network approach for COVID-19 disease detection
    (American Physiological Society, 2020) Irmak, Emrah
    In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 versus normal versus pneumonia) with 98.27% average accuracy. The hyperparameters of both CNN models are automatically determined using Grid Search. Experimental results on large clinical data sets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome the disadvantages mentioned above. Moreover, the proposed CNN models are fully automatic in terms of not requiring the extraction of diseased tissue, which is a great improvement of available automatic methods in the literature. To the best of the author’s knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN, whose hyperparameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN-based COVID-19 chest X-ray image classification study that uses the largest possible clinical data set. A total of 1,524 COVID-19, 1,527 pneumonia, and 1524 normal Xray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper. © 2020 the American Physiological Society.
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    Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework
    (2021) Irmak, Emrah
    Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author's knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper-parameters are tuned by the grid search optimizer. The proposed CNN models are compared with other popular state-of-the-art CNN models such as AlexNet, Inceptionv3, ResNet-50, VGG-16 and GoogleNet. Satisfactory classification results are obtained using large and publicly available clinical datasets. The proposed CNN models can be employed to assist physicians and radiologists in validating their initial screening for brain tumor multi-classification purposes.