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Öğe Comparison of posterior cranial fossa morphometric measurements in Chiari type I patients with and without syrinx cavity on magnetic resonance imaging(Int Scientific Information Inc, 2022) Dogan, Gulec Mert; Sigirci, Ahmet; Tetik, Bora; Pasahan, Ramazan; Onal, Cagatay; Arslan, Ahmet K.Purpose: To compare the posterior fossa measurements of Chiari type I malformation (CHM1) patients with and without syrinx and with a control group. Material and methods: The patients with syrinx were divided into 2 groupd according to syrinx width/cord width (S/C) ratios: group 1 - S/C ratio < 50%; group 2 - S/C ratio > 50%. The length of the clivus, the AP length of the foramen magnum, the AP length of the posterior fossa, the perpendicular distance between the McRae line and (a) the splenium of corpus callosum, (b) the pons, and (c) the fastigium of the 160 patients and of the 160 control patients were statistically compared. In addition, the measurements of the patients with and without syrinx, according to the S/C ratio, were statistically compared. Results: Syrinx was present in 59 (36.8 %) of the 160 patients. The S/ C ratio was < 50% in 30 (50.9 %) of them, and S/C ratio > 50% in 29 (49.1%) of them. All the measurements in the patient group, except of the AP length of the foramen magnum, were statistically significantly lower than in the control group (p = 0.001). There was no significant difference in the measurements of the patients with syrinx group 1 and the patients without syrinx, but the AP length of posterior fossa was statistically significantly lower in the patients with syrinx group 2 than the patients without syrinx (p = 0.03). Conclusion: The S/C ratio can be a guide to the underlying aetiology.Öğe Deep learning based-classification of dementia in magnetic resonance imaging scans(Ieee, 2019) Ucuzal, Hasan; Arslan, Ahmet K.; Colak, CemilDeep learning is much preferred in image processing applications since it can give fast and important results. This research aims at developing an open source software for deep learning based-classification of dementia in magnetic resonance imaging scans. Keras (i.e., a deep-learning framework) is employed for constructing a deep learning based-model that could be discriminate between dementia patients and healthy individuals. The achieved findings demonstrate that the proposed system can be used to detect individuals with suspected dementia disease.Öğe Estimation of risk factors associated with colorectal cancer: an application of knowledge discovery in databases(Academic Publication Council, 2016) Firat, Feyza; Arslan, Ahmet K.; Colak, Cemil; Harputluoglu, HakanColorectal cancer is one of the first reasons for death due to cancer in the world. The goal of this study is to predict important risk factors of colorectal cancer (CRC) by knowledge discovery in databases (KDD) methods. This study comprised a retrospective CRC data of patients who had been diagnosed with colorectal cancer. The selected records between 1 January 2010 and 1 March 2014 were collected randomly from Turgut Ozal Medical Centre databases. The study included 160 individuals: 80 patients admitted to Department of Oncology and diagnosed with CRC, and 80 control subjects with non-CRC categorization. The groups were matched for age and gender. We mined retrospective CRC data from large integrated health systems with electronic health records. Specific demographical and clinical variables including calcium, hemoglobin, white blood cells, platelets, potassium, sodium, glucose, creatinine and total bilirubin were used in multilayer perceptron (MLP) artificial neural networks (ANN) modeling. In this study, patient and control groups consist of 160 individuals. In each group, 45 of these (56.3%) are male, and 35 (43.7%) are women. Mean age of CRC patients and control groups is 58.6 +/- 13.0. While the accuracy was 71.31% in training dataset (n=122), the accuracy was 81.82% in testing dataset. Area under curve (AUC) values of training and testing datasets were 0.73 and 0.81, respectively. The suggested MLP ANN model identified significant factors of calcium, creatinine, potassium, platelets, sodium, hemoglobin and total bilirubin. Taken together, the suggested MLP ANN model might be used for the estimation of risk factors associated with CRC as an application of medical KDD.Öğe Prediction of Melanoma from Dermoscopic Images Using Deep Learning-Based Artificial Intelligence Techniques(Ieee, 2019) Kaplan, Ali; Guldogan, Emek; Colak, Cemil; Arslan, Ahmet K.Recently, hospitals and health care institutions have increasingly been addressing clinical decision support systems (CDSS), which can offer specific patient assessments or recommendations to physicians and health care professionals. It is very useful to develop CDSS which can help physicians to make meaningful and correct decisions by using existing data or image sets. Also, CDSS increases the diagnostic accuracy of diseases, provides significant facilities in precision medicine applications, increases operating efficiency of hospitals and reduces costs. In this context, the proposed project intends to create a model usingpre-trained networks (i.e. VGG-16,) based on deep learning (DL) that can successfully predict the melanoma using dermoscopic images. The current study provides clinical support to physicians in the medical decision-making process for the diagnosis of melanoma.
