Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Start date: 2020search.filters.applied.f.language: search.filters.language.tur

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Now showing 1 - 10 of 19
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    Automatic Glacoma Detection Using Whale Optimization and Support Vector Machines
    (2022) Ozen, Sinasi Kutay; Aksahin, Mehmet Feyzi
    Glaucoma is among the most common causes of permanent blindness in humans. The mass screening will aid in early diagnosis in a large population, as the initial symptoms are not obvious. This type of mass screening requires an automated diagnostic technique. Our proposed automation extracts feature by obtaining disk-to-cup ratio by applying histogram equalization, median filter, otsu thresholding, and whale optimization algorithm, respectively, on the optic disc region obtained by preprocessing. In addition, the optic disc circumference, optic disc area, optic cup circumference, and optic cup area values obtained from the optic disc region are given to the support vector machine model together with the cup-disc ratio, and glaucoma detection is made automatically. The proposed system has been validated on a real ophthalmological images of both normal and glaucoma cases. The results show the effectiveness of the proposed method when compared with other existing systems.
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    Detection of Visual Impairment From Retinal Fundus Images with Deep Learning
    (2022) Olcer, Didem; Erdas, Cagatay Berke
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    Prediction of Frailty Grade Using Machine Learning Models
    (2022) Erdas, Cagatay Berke; Olcer, Didem
    Nowadays, frailty is becoming a major issue for the aging population. Frailty grading is important for patient quality of life because it is a geriatric syndrome of decreased physiological reserve that leads to increased susceptibility to physical stress factors and susceptibility to cardiovascular diseases. In this context, this study seeks a solution to the fragility rating regression problem with K Nearest Neighbor, Decision Tree, Random Forest, Extra Trees and CatBoost models using time domain features extracted from the triaxial accelerometer signals collected during the TUG test. Moreover, estimating the grade of frailty involved in addition to diagnosing people with the disease will provide physicians with more detailed information about the patient and allow accurate and effective treatment/supportive treatment.
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    Journal Finder for TRDIZIN: Baseline Study
    (2021) Demirkan, Mert; Ozgur, Atilla; Erdem, Hamit; https://orcid.org/0000-0002-1396-2060; https://orcid.org/0000-0002-9237-8347; https://orcid.org/0000-0003-1704-1581; AAD-6546-2019
    One of the main steps in publication of a paper is finding a related journal for the work of the researchers. In the recent years, there have been an increase in scientific papers publications. This situation leads the introduction of journal recommender systems by leading academic publishers. Without using a journal recommender system, this step would be a very time consuming task. This study reviewed similar studies in the literature. Current study is the first version' of journal recommender system for TRDIZIN index which has an increasing amount of articles. A dataset is created by collecting titles, keywords, and abstracts of papers from dergipark web page. Using the collected dataset, a target journal from TRDIZTN is suggested according to title, abstract and keyword of the given article. For the first version of the journal recommender system, cosine similarity is used. The results of the suggested algorithm are evaluated by using performance criteria as the nearest 5 and 10 journals' accuracy.
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    The Effect of Uncertainties in Skin Layers on Photoacoustic Imaging of Skin Cancer
    (2020) Yucel, Hazel; Ozdemir, Ozgur; 0000-0001-9617-3747; AAJ-1090-2021
    Photoacoustic imaging has started to be used as a new modality for imaging cancerous tissues in layered skin tissue consisting of epidermis, dermis and hypodermis layers. In the imaging algorithms used in photoacoustic imaging, the layered environment Green function is calculated by assuming that the thickness of each layer is known and imaging is performed. However, the value of these layer thicknesses varies from person to person and it is not possible to know the actual values during imaging. In this study, what kind of errors in the estimation of skin layer thicknesses will lead to the errors in photoacoustic imaging is investigated by using Back-projection imaging algorithm. For this purpose, the error rates at the site of cancerous tissue were determined due to changes in layer thicknesses in layer structures with different acoustic properties.
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    Heart Disease Prediction by Using Machine Learning Algorithms
    (2020) Erdogan, Alperen; Guney, Selda
    Nowadays, one of the most important illness is heart disease which cause of mostly patients dead. Medical diagnosis of heart diseases is very difficult. While heart diseases are diagnosed medically, they can be confused with other diseases that show same symptoms such as chest pain, shortness of breath, palpitations and nausea. This makes it difficult to diagnose heart diseases medically. In this study, the presence of heart diseases was determined by using machine learning algorithms. In this study, the data obtained from the patients were weighted according to their effects on the success rate. In this study, a method is proposed for determine weight coefficient. According to proposed method's results, 86,90% success was achieved with 13 different features obtained from the patients.
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    Automatic Brain Tissue Segmentation on TOF MRA Image
    (2020) Ozen, Sinasi Kutay; Aksahin, Mehmet Feyzi
    For the segmentation of brain vessels from MRA images, brain tissue is used in the head, eye, skull, etc. must be separated from the structures. For this reason, studies are carried out for the segmentation of brain tissue. In this study, the method that automatically segregates brain tissue from magnetic resonance angiography images taken with time of flight (TOF) technique is presented. The method in the study consists of live steps. First of all, the tip contrast values in the image are filtered by anisotropic diffusion filtering method. Parameters of anisotropic diffusion method are determined automatically by the natural image quality evaluator method. Sudden density transitions arc detected by applying LoG edge detection filter on the filtered image. It is made ready for image analysis by applying etching on the image with density transitions. According to the conditions determined in image analysis, brain tissue is obtained separated from other head structures. As a result of this study, an easy-to-apply, fast-delivering, high-accuracy automatic algorithm has been introduced.
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    Idfatification Using FCG Signals
    (2020) Kilicer, Elif Cansu; Ay, Sevval; Aksahin, Mehmet Feyzi
    Systems that determine identity with individual features are called biometric systems. Today, voice, fingerprint, retina/iris, and facial recognition systems are some of the biometric identification methods. These methods have become replicable with the advancement of technology. Accordingly, Electrocardiogram (ECG) signals are universal, unique, easy to measure, and can only be obtained from living people. For this reason, it can be accepted that ECG is an effective method that can he used to prevent counterfeiting among biometric identification methods. In this study, an algorithm that can make identification via ECG is proposed. Within the scope of the study, the time and time-frequency domain analyzes of the ECG signals obtained from the PhsiyoNet database are performed then various features are determined. These determined features were classified using machine learning methods. The performance of the developed algorithm has been calculated as 100% accuracy, 1000/0 specificity, and 100% sensitivity.
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    A Machine Learning Based Approach to Detect Survival of Heart Failure Patients
    (2020) Erdas, Cagatay Berke; Olcer, Didem
    One of the diseases with high prevalence among the consequences of cardiovascular diseases is heart failure. Heart failure is a condition in which the muscles in the heart wall become faded and dilated, limiting the heart's ability to pump blood. As time passes, the heart cannot meet the proper blood requirement in the body, and as a result, the person has difficulty breathing. As the human age increases, the incidence of heart failure gradually increases, and the rate of mortality due to heart failure also increases. In this context, close monitoring of people suffering from this disease will significantly increase the survival rate. In this study, a machine learning-based system is proposed to predict the mortality-survival status of patients with heart failure. Thus, by identifying people with mortality risk, the survival probability of the patients may increase with more effective and close follow-up.
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    A Deep Learning Based Approach to Detect Neurodegenerative Diseases
    (2020) Erdas, Cagatay Berke; Sumer, Emre; AGA-5711-2022
    Studies conducted by the World Health Organization (WHO) show that more than a billion people worldwide suffer from neurological disorders and the lack of effective diagnostic procedures affects treatment. Characterizing specific motor symptoms to facilitate their diagnosis can he useful in monitoring disease progression and effective treatment planning. Classification of highly prevalent neurodegenerative diseases (NDD) such as Parkinson's disease (PH), Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease (HH) is of clinical importance, One of the methods used in the literature to detect these neurodegenerative diseases is gait analysis-based classification. in this study, 12 different features fed a unidimensional Convolutional Neural Network (CNN) deep learning algorithm-based model, and aims to detect PD, HD, and ALS diseases was studied. The unidimensional CNN deep learning model fed with 12 features achieved 78,92%, 84,39% and 92,09% classification accuracy for control against HH, control. against PH, and control detection problems against ALS. Again, the relevant classifier produced 84,75% accuracy with the approach developed to separate all neurodegenerative disease specimens (NDD) under a single label as a class, and to distinguish these diseases against the current control.