MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers.

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

3M-Brazzein as a Natural Sugar Substitute Attenuates Obesity, Metabolic Disorder, and Inflammation.

Obesity is a global chronic disease linked to various diseases. Increased consumption of added sugars, especially in beverages, is a key contributor to the obesity epidemic. It is essential to reduce or replace sugar intake with low-calorie sweeteners. Here, a natural sweet protein, 3M-brazzein, was investigated as a possible sugar substitute. Mice were exposed to 3M-brazzein or 10% sucrose of equivalent sweetness, in drinking water to mimic human obesity development over 15 weeks. Consumption of 3M-brazzein in liquid form did not cause adiposity hypertrophy, resulting in 33.1 ± 0.4 g body weight and 0.90 ± 0.2 mm fat accumulation, which were 35.9 ± 0.7 g (p = 0.0094) and 1.53 ± 0.067 mm (p = 0.0031), respectively, for sucrose supplement. Additionally, 3M-brazzein did not disrupt glucose homeostasis or affect insulin resistance and inflammation. Due to its naturally low-calorie content, 3M-brazzein could also be a potential sugar substitute that reduces adiposity.