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According to World Health Organization reports, the number of deaths due to brain tumors is increasing day by day. As with all diseases, early diagnosis is very important in brain tumors. Diagnosis of brain tumors from MRI images by physicians can cause both loss of time and erroneous interpretations. Therefore, computer-aided automatic detection systems have become an important research topic to minimize time loss and error margin. In this study, the proposed model aims to classify brain tumors. For this purpose, a four-class data set was used to classify brain tumors using a transfer learning based Inception-ResNet-V2 convolutional neural network model. The obtained results were tested on a dataset of 1621 gliomas, 1645 meningiomas, 1757 pituitary glands and 2000 normal brain images using a 5-fold cross validation technique and an average accuracy of 99.5% was achieved. [ABSTRACT FROM AUTHOR]

MR spectroscopy (MRS) is a critical imaging modality in brain tumor detection, in which brain metabolites other than water and fat can be measured. Along with the advances in artificial intelligence and data mining, it is seen that computer aided diagnosis (CAD) systems are widely used in medicine. The high achievements achieved have a positive effect on medical diagnosis and treatment processes by providing time saving for CAD applications. Deep learning, based on classical classification methods, the results are more successful on the data, and with high classification ability is the artificial intelligence algorithm. In this study, it was aimed to develop a CAD system to assist physicians in tumor detection by using the ratios of Choline (Cho), creatine (Cr) and N-Acetyl Aspartate (Naa) metabolites (Cho/Cr, Cho/Naa and Naa/Cr) obtained by MRS. For classification data, four classical data classification methods were used, which are deep learning application, Long Short-Term Memory (LSTM) and comparison. While the class performance results obtained with K-Nearest Neighborhood (KEYK), Support Vector Machine (SVM) and Decision Trees (DT) were found to be around 83±6,36 % from classical methods, this value was obtained as 93% with the LSTM-based deep learning method proposed in the study. This shows that deep learning can be used in a CAD system that can assist the experts in the relevant field in the decision-making process in classifying MRS data by demonstrating high classification performance. [ABSTRACT FROM AUTHOR]

Aim:To reduce operator dependency and achieve greater accuracy, the computer-aided diagnosis (CAD) systems are becoming a useful tool for detecting noninvasively and determining tissue characterization in medical images. We aimed to suggest a CAD system in discriminating between benign and malignant breast masses.Methods:The dataset was composed of 105 randomly breast magnetic resonance imaging (MRI) including biopsy-proven breast lesions (53 malignant, 52 benign). The expectation-maximization (EM) algorithm was used for image segmentation. 2D-discrete wavelet transform was applied to each region of interests (ROIs). After that, intensity-based statistical and texture matrix-based features were extracted from each of the 105 ROIs. Random Forest algorithm was used for feature selection. The final set of features, by random selection base, splatted into two sets as 80% training set (84 MRI) and 20% test set (21 MRI). Three classification algorithms are such that decision tree (DT, C4.5), naive bayes (NB), and linear discriminant analysis (LDA) were used. The accuracy rates of algorithms were compared.Results:C4.5 algorithm classified 20 patients correctly with a success rate of 95.24%. Only one patient was misclassified. The NB classified 19 patients correctly with a success rate of 90.48%. The LDA Algorithm classified 18 patients correctly with a success rate of 85.71%.Conclusion:The CAD equipped with the EM segmentation and C4.5 DT classification was successfully distinguished as benign and malignant breast tumor on MRI.

Ülkemizde küçükbaş hayvan sayısı her geçen gün çeşitli sebeplerden dolayı azalmaktadır. Küçükbaş hayvan sayısının azalmasına paralel olarak, hayvansal üretimde de önemli azalmalar görülmektedir. Küçükbaş hayvan sayısının azalmasını önlemenin bir yolu da hastalıklarla ilgili tahmin ve analizlerin başarılı bir şekilde yapılabilmesidir. Makine öğrenmesi ile yapılan bilgisayar destekli tanı çalışmaları sayesinde, sağlık hizmetlerinin kalitesi artarken sağlık sektöründeki maliyetler azalmaktadır. Bu çalışmanın amacı makine öğrenmesi yöntemleri ile kuzularda erken hastalık teşhisi yapmaktır. Bunun için çalışmada karar ağaçları, saf bayes, k-en yakın komşu, yapay sinir ağları ve rassal orman yöntemleri kullanılmıştır. Bu sınıflandırma yöntemlerinin performansları doğruluk, dengeli doğruluk, seçicilik, duyarlılık, F-ölçütü, kappa ve ROC eğrisi altında kalan alan (AUC) ölçütleri ile analiz edilmiştir. Çalışma sonucunda bilgisayar destekli tanı için Saf bayes yöntemi diğer yöntemlerden daha başarılı sonuçlar üretmiştir. Basit ve uygulaması kolay olan Saf bayes yöntemin diğer karmaşık yöntemlerden daha başarılı sonuçlar elde etmesi oldukça önemlidir.

Ülkemizde küçükbaş hayvan sayısı her geçen gün çeşitli sebeplerden dolayı azalmaktadır. Küçükbaş hayvan sayısının azalmasına paralel olarak, hayvansal üretimde de önemli azalmalar görülmektedir. Küçükbaş hayvan sayısının azalmasını önlemenin bir yolu da hastalıklarla ilgili tahmin ve analizlerin başarılı bir şekilde yapılabilmesidir. Makine öğrenmesi ile yapılan bilgisayar destekli tanı çalışmaları sayesinde, sağlık hizmetlerinin kalitesi artarken sağlık sektöründeki maliyetler azalmaktadır. Bu çalışmanın amacı makine öğrenmesi yöntemleri ile kuzularda erken hastalık teşhisi yapmaktır. Bunun için çalışmada karar ağaçları, saf bayes, k-en yakın komşu, yapay sinir ağları ve rassal orman yöntemleri kullanılmıştır. Bu sınıflandırma yöntemlerinin performansları doğruluk, dengeli doğruluk, seçicilik, duyarlılık, F-ölçütü, kappa ve ROC eğrisi altında kalan alan (AUC) ölçütleri ile analiz edilmiştir. Çalışma sonucunda bilgisayar destekli tanı için Saf bayes yöntemi diğer yöntemlerden daha başarılı sonuçlar üretmiştir. Basit ve uygulaması kolay olan Saf bayes yöntemin diğer karmaşık yöntemlerden daha başarılı sonuçlar elde etmesi oldukça önemlidir.

Computer assisted diagnosis (CAD) systems have been used frequently in recent years in order to create a doctoral assistance decision support system using various patient information. In this study, it was aimed to compare the success of the Wechsler Intelligence Scale for Children (WISC-R) profiles by the decision trees algorithm applied to the CAD system, and the classification success in the detection of the border intelligence (BI), mild and moderate intellectual disability (ID). The data set of the study was formed by using WISC-R test results of 132 patients (50 BI, 61 mild ID and 21 moderate ID) diagnosed according to DSM-V. In order to compare the effect of WISC-R scores on the outcome, 132 patients' test scores: total, verbal and performance intelligence scores, verbal and performance intelligence subscale scores and 3 separate data sets were formed. The all the score types of the WISCR test, the first two node total intelligence scores were selected, and 108 of 132 patients were classified regardless of another attribute. In the decision tree, the first and third datasets containing the total intelligence section score type achieved a high classification success rate of 0.91. The results of this study show that the total intelligence score of the WISC-R profiles is the most effective in the decision trees algorithm in BI, mild and moderate ID diagnosis, and that CAD systems are possible. [ABSTRACT FROM AUTHOR]

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In our coun try, the number of small ruminant animals is decreasing day by day due to various reasons. In parallel with the decrease in the number of small ruminants, significant decreases are seen in animal production. One way to prevent the reduction in the number of small ruminants is to be able to make successful predictions and analysis related to the diagnosis. Thanks to computer-aided diagnostic studies performed with machine learning, the quality of health services increases while the costs of the health sector decrease. The aim of this study is to perform computer aided diagnosis in neonatal lambs using machine learning methods. Hence in study, decision tree, naive bayes, k-nearest neighbors, artificial neural networks and random forest methods were used. The performances of these classification methods were analyzed with accuracy, balanced accuracy, specifity, recall, F-measure, kappa and area under the ROC curve (A UC) criteria. As a result of the study, the Naive bayes method more successful results than other methods for computer aided diagnosis produced. It is very important tha t, the Naive bayes method is simple and easy to apply, achieves more successful results than other complex methods. [ABSTRACT FROM AUTHOR]

American Psychiatry Association has scheduled to release The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) in May 2013. According to the main changes being proposed about autism, there will be one unified Autism Spectrum Disorder diagnosis in the DSM-5 classification. This unified diagnosis will eliminate the distinct diagnostic categories under Pervasive Developmental Disorders in the DSM-IV-TR, namely autistic disorder, asperger syndrome, pervasive developmental disorder-not otherwise specified, and childhood disintegrative disorder. Rett syndrome will be excluded from autism spectrum disorder due to its genetic basis. In addition, severity of symptoms will be measured among individuals with autism spectrum disorder based on the support level required due to the impairment in their lives. The basic rationale behind this revision is that it is better to conceptualize autism as a spectrum including various individuals whose symptoms in different developmental areas range from mild to severe. It is aimed to increase the specificity of autism diagnosis by using one single diagnostic category with its specified severity rather than differentiating several subtypes. The major concern raised over the DSM-5 proposal has been the possibility that some of the individuals who were diagnosed with pervasive developmental disorder according to the DSM-IV-TR might not get a diagnosis in this new system. After the DSM-5 is released, clinical, legal, and educational rearrengements regarding the use of new autism spectrum disorder diagnostic criteria are expected to accelerate worldwide and in Turkey. This paper aims to review briefly the upcoming autism spectrum disorder diagnosis planned to appear in the DSM-5, the rationale of the proposed revision, main critics to the DSM-5 draft that has been publicized, and some of the regulations expected to occur in practice after the changes. [ABSTRACT FROM AUTHOR]