No abstract

Alzheimer's disease is a progressive neurodegenerative fatal disease characterized by a decrease in mental functions. Although there is no definitive treatment for the disease, there are some treatment methods that delay the course of the disease in case of early diagnosis. Therefore, early diagnosis and classification of the disease are important to determine the most appropriate treatment. The most commonly used method for imaging the brain with a high soft‐tissue resolution is magnetic resonance imaging (MRI). Brain MRI help in the diagnosis of Alzheimer's disease with some specific imaging findings. In this study, we aimed to classify Alzheimer's disease in brain MRI using machine learning architectures. An mRMR‐based hybrid CNN was proposed in the study. First, features of MRI in Darknet53, InceptionV3, and Resnet101 models were extracted. These extracted features were concatenated. Then the obtained features were optimized using the mRMR method. SVM and KNN classifiers were used to classify the optimized features. The accuracy value obtained in the proposed model was 99.1%.

No abstract

Objective: The aim of this study was to determine the prevalence of mesenteric panniculitis (MP) and to describe its clinical characteristics, therapy, and outcome. Subjects and Methods: This retrospective study was carried out among patients with MP based on computed tomography (CT) scans from January 2012 to December 2015. The CT images were reanalyzed by study radiologists to confirm the previous MP diagnosis. Patients were divided into 2 groups, i.e., idiopathicandsecondary, based on the presence or absence of associated predisposing factors such as trauma, malignancy, autoimmune disorders, ischemia, or previous abdominal surgery. The clinical characteristics of the 2 groups, as well as treatments, were assessed. Results: Among the 19,869 CT scans, 36 patients (0.18%) with MP were identified (i.e., 19 [53%] females and 17 [47%] males). The median age was 54 years (range 26 - 76). Twenty-four patients (67%) were categorized into the idiopathic group. Malignancy was the predisposing factor in 8 (22%) of those patients. Furthermore, abdominal pain was the cardinal symptom observed in 22 patients (92%) in the idiopathic group. In the idiopathic group, 15 patients (63%) were treated with antibiotics and 16 (67%) were treated with nonsteroidal anti-inflammatory drugs (NSAID). One unresponsive patient was treated with colchicine. Symptomatic relief was achieved in all of the treated patients. Conclusion: In thisstudy, a symptomatic idiopathic subgroup of patients with MP did not have any associated disorder. The response to treatment with antibiotics and NSAID was effective in most of the patients. Based on these findings, anti-inflammatory treatments beyond NSAID and surgery should be reserved for patients who are unresponsive to antibiotics and NSAID.

Pneumonia is a disease caused by inflammation of the lung tissue that is transmitted by various means, primarily bacteria. Early and accurate diagnosis is important in reducing the morbidity and mortality of the disease. The primary imaging method used for the diagnosis of pneumonia is lung x-ray. While typical imaging findings of pneumonia may be present on lung imaging, nonspecific images may be present. In addition, many health units may not have qualified personnel to perform this procedure or there may be errors in diagnoses made by traditional methods. For this reason, computer systems can be used to prevent error rates that may occur in traditional methods. Many methods have been developed to train data sets. In this article, a new model has been developed based on the layers of the ResNet50. The developed model was compared with the architectures InceptionV3, AlexNet, GoogleNet, ResNet50 and DenseNet201. In the developed model, the maximum accuracy rate was achieved as 97.22%. The model developed was followed by DenseNet201, ResNet50, InceptionV3, GoogleNet and AlexNet, respectively, according to their accuracy. With these developed models, the diagnosis of pneumonia can be made early and accurately, and the treatment management of the patient will be determined quickly.