Research Article
Abstract
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Brain tumors are a type of cancer that is usually diagnosed by biopsy after surgery. In this study, we propose two different methods to classify, diagnose, and predict brain tumors from magnetic resonance imaging (MRI) images using deep learning (DL) techniques. We use a convolutional neural network (CNN) based on a visual geometry group with 19 convolutional layers (VGG-19), a pre-trained model for image recognition, to classify MRI images into two classes: yes and no tumors. In this paper, we also use an artificial neural network (ANN) transfer learning (TL) approach to predict the type of tumor from four classes: meningioma, glioma, pituitary, or no-tumor. We preprocess and augment the MRI images before feeding them to the models. We evaluate the models using an accuracy metric and compare them with VGG-16 and other methods. The main approach of this paper is to classify, diagnose, and predict brain tumors in the lowest run time and the acceptable rate of accuracy with VGG-19 + TL and ANN + TL techniques. Based on the resulting paper, our proposed models achieve 91.26% and 91% overall accuracy, and outperform the compared methods in terms of high total accuracy, and the lowest run time.
Case Report
Abstract
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Background: Cirrhosis is a chronic disease with multiple etiologies, multiple manifestations, and multi-organ involvement. It affects about 200 million people worldwide and its main complication is portal hypertension (PHT). Cirrhotic cardiomyopathy is a consequence of PHT, with heart failure (HF) being the main manifestation. On the other hand, for people with cardiopathy due to reasons beyond cirrhosis, the presence of the last could act as a trigger to acute HF. Finally, when HF and cirrhosis coexist, the renal system suffers the most, originating from type 5 cardiorenal syndrome, a complex entity with many challenges in diagnosis and treatment. This entity was recently defined and there is a lack of data on the epidemiology of this syndrome.
Case Description: A 77-year-old woman, with cardiovascular risk factors; heart failure with reduced ejection fraction (HFrEF), secondary ischemic and valvular cardiopathy, chronic kidney disease (CKD), and stable decompensated cirrhosis, went to the emergency department after progressive abdominal perimeter augmentation, leg edema, and hematochezia. It was assumed acute decompensated cirrhosis and acute decompensated CKD, had triggered digestive bleeding and the patient stayed hospitalized. Intravenous diuretic therapy was started but the ascites and kidney function got worse, the NT-proBNP grew until 1125000 pg/ml and transthoracic echocardiography showed important signs of congestion, isolated right ventricular dysfunction, with normal cardiac output. After teamwork’s discussion, a type 5 cardiorenal syndrome was assumed deciding not to act on tricuspid regurgitation due to the maladaptive RV remodeling. Hepatorenal syndrome (HRS) treatment was begun (oral terlipressin and intravenous albumin), followed by decongestion therapy, resulting in a marked improvement in clinical presentation. The kidney, heart, and liver recovered to these basal states.
Conclusion: With this clinical case, the authors want to show how multidisciplinary management is important to face tricuspid regurgitation and right ventricular dysfunction.
