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Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done.

Opinion mining is a recent discipline combining Information Retrieval and Computational Linguistics which is concerned with the opinion a document expresses and not just with the topic in the document. Online forums, newsgroups, blogs, and specialized sites provide voluminous information feeds from where opinions can be retrieved. Opinion's polarity is established through application of machine learning techniques for classification of textual reviews as either a positive or negative class. In this paper, it is proposed to extract the feature set from reviews using Inverse document frequency and the reviews are classified as positive or negative using Bagging algorithms. The proposed method is evaluated using a subset of Internet Movie Database (IMBd).

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