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

IMPACT OF PEER ASSISTED GALS LEARNING ON PRECISION IN CLINICAL DIAGNOSIS OF VARIOUS SPINAL DISORDERS

GALS screening examination is a simple, easily reproducible technique which requires minimal clinical instrument. This serves as effective tool in clinical diagnosis and assessment of various disorders. Spine Disorders forms integral part of Outdoor Patient Department (OPD) and Indoor patient department of Ayurvedic hospitals. They also forms integral part of various PG dissertations. Clinical diagnosis of spine disorder is very important. In spite of such enormous burden in OPD & IPD many times Ayurvedic students lack in diagnostic precision of various spinal disorders. Assistance of peer or teacher is very important in learning clinical skill. Aim of present study was to see impact of peer assisted GALS learning on precision in clinical diagnosis and assessment in interns and first year PG students.Total 15 students who were working in unit 3 of Kayachikitsa department were selected for training. 1 months peer assisted GALS skill training was given to them. They were assessed for precision in clinical diagnosis and confidence to diagnose with the help of confidence questionnaire and Final Objective structured clinical examination (OSCE) before and after training along with that open ended response also taken from them about the examination. Data was collected and analyzed based on observations and inference drawn. Peer assisted GALS learning improves clinical performance of students. GALS is a effective clinical tool in clinical diagnosis and assessment of various spinal disorders

Design & Development of Bidirectional Solar Tracking System Implemented in Western Region of Maharashtra",

Abstract: -Solar energy systems have emerged as a viable source of renewable energy over the past two or three decades, and are now widely used for a variety of industrial and domestic applications. Such systems are based on a solar collector, designed to collect the sun's energy and to convert it into either electrical power or thermal energy. This paper presents the design & development of bi-directional solar tracking system. Solar tracking allows more energy to be produced because the solar array is able to remain aligned to the sun. The constructed device was implemented by integrating it with 900V inverter and 12volts, 100AH battery. Due to the atmosphere the sun energy is not as great in the morning and evening compared to noontime, which initiated the development of solar tracker