Multiclass Classification of Brain MRI through DWT and GLCM Feature Extraction with Various Machine Learning Algorithms

This study delves into the domain of medical diagnostics, focusing on the crucial task of accurately classifying brain tumors to facilitate informed clinical decisions and optimize patient outcomes. Employing a diverse ensemble of machine learning algorithms, the paper addresses the challenge of multiclass brain tumor classification. The investigation centers around the utilization of two distinct datasets: the Brats dataset, encompassing cases of High-Grade Glioma (HGG) and Low-Grade Glioma (LGG), and the Sartaj dataset, comprising instances of Glioma, Meningioma, and No Tumor. Through the strategic deployment of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) features, coupled with the implementation of Support Vector Machines (SVM), k-nearest Neighbors (KNN), Decision Trees (DT), Random Forest, and Gradient Boosting algorithms, the research endeavors to comprehensively explore avenues for achieving precise tumor classification. Preceding the classification process, the datasets undergo pre-processing and the extraction of salient features through DWT-derived frequency-domain characteristics and texture insights harnessed from GLCM. Subsequently, a detailed exposition of the selected algorithms is provided and elucidates the pertinent hyperparameters. The study's outcomes unveil noteworthy performance disparities across diverse algorithms and datasets. SVM and Random Forest algorithms exhibit commendable accuracy rates on the Brats dataset, while the Gradient Boosting algorithm demonstrates superior performance on the Sartaj dataset. The evaluation process encompasses precision, recall, and F1-score metrics, thereby providing a comprehensive assessment of the classification prowess of the employed algorithms

A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement

Removing background noise from acoustic observations to obtain clean signals is an important research topic regarding numerous real acoustic applications. Owing to their strong model capacity in function mapping, deep neural network-based algorithms have been successfully applied in target signal enhancement in acoustic applications. As most target signals carry semantic information encoded in a hierarchal structure in short-and long-term contexts , noise may distort such structures nonuniformly. In most deep neural network-based algorithms, such local and global effects are not explicitly considered in a modeling architecture for signal enhancement. In this paper, we propose a temporal attentive-pooling (TAP) mechanism combined with a conventional convolutional recurrent neural network (CRNN) model, called TAP-CRNN, which explicitly considers both global and local information for acoustic signal enhancement (ASE). In the TAP-CRNN model, we first use a convolution layer to extract local information from acoustic signals and a recurrent neural network (RNN) architecture to characterize temporal contextual information. Second, we exploit a novel attention mechanism to contextually process salient regions of noisy signals. We evaluate the proposed ASE system using an infant cry da-taset. The experimental results confirm the effectiveness of the proposed TAP-CRNN, compared with related deep neu-ral network models, and demonstrate that the proposed TAP-CRNN can more effectively reduce noise components from infant cry signals with unseen background noises at different signal-to-noise levels. Impact Statement-Recently proposed deep learning solutions have proven useful in overcoming certain limitations of conventional acoustic signal enhancement (ASE) tasks. However, the performance of these approaches under real acoustic conditions is not always satisfactory. In this study, we investigated the use of attention models for ASE. To the best of our knowledge, this is the first attempt to successfully employ a convolutional recurrent neural network (CRNN) with a temporal attentive pooling (TAP) algorithm for the ASE task. The proposed TAP-CRNN framework can practically benefit the as-sistive communication technology industry, such as the manufacture of hearing aid devices for the elderly and students. In addition, the derived algorithm can benefit other signal processing applications, such as soundscape information retrieval, sound environment analysis in smart homes, and automatic speech/speaker/language recognition systems. Index Terms-Acoustic signal enhancement, convolutional neural networks, recurrent neural networks, bidirectional long-short term memory