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

Customized CNN Model for Multiple Illness Identification in Rice and Maize

Crop diseases imperil global food security and economies, demanding early detection and effective management. Convolutional Neural Networks (CNNs), particularly in rice and maize leaf disease classification, have gained traction due to their automatic feature extraction capabilities. CNN models eliminate manual feature extraction, enabling precise disease diagnosis based on learned features. Researchers have rapidly advanced these models, achieving promising results. Leaf disease characteristics like color changes, texture variations, and lesion appearance have been identified as useful for automated diagnosis using machine learning. Developing CNN models involves crucial stages: dataset preparation, architecture selection, hyperparameter tuning, and model training and evaluation. Diverse and accurately annotated datasets are pivotal, and appropriate CNN architecture selection, such as ResNet101 and XceptionNet, ensures optimal performance. These architectures' pre-training on vast image datasets enhances feature extraction. Hyperparameter tuning fine-tunes the model, and training and evaluation gauge its precision. CNN models hold potential to enhance rice and maize productivity and global food security by effectively detecting and managing diseases

Vlsi Implementation of Olfactory Cortex Model

This thesis attempts to implement the building blocks required for the realization of the biologically motivated olfactory neural model in silicon as the special purpose hardware. The olfactory model is originally developed by R. Granger, G. Lynch, and Ambros-Ingerson. CMOS analog integrated circuits were used for this purpose. All of the building blocks were fabricated using the MOSIS service and tested at our site. The results of this study can be used to realize a system level integration of the olfactory model.Electrical Engineerin

Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier

Soybean fungal diseases such as Blight, Frogeye leaf spot and Brown Spot are a significant threat to soybean plant due to the severe symptoms and lack of treatments. Traditional diagnosis of the thease diseases relies on disease symptom identification based on neaked eye observation by pathalogiest, which can lead to a high rate of false-recognition. This work present a novel system, utilizing multiclass support vector machine and KNN classifiers, for detection and classification of soybean diseases using color images of diseased leaf samples. Images of healthy and diseased leaves affected by Blight, Frogeye leaf spot and Brown Spot were acquired by a digital camera. The acquired images are preprocessed using image enhancement techniques. The background of each image was removed by a thresholding method and the Region of Interest (ROI) is obtained. Color-based segmentation technique based on K-means clustering is applied to the region of interest for partitioning the diseased region. The severity of disease is estimated by quantifying a number of pixels in the diseased region and in total leaf region. Different color features of segmented diseased leaf region were extracted using RGB color space and texture features were extracted using Gray Level Co-occurrence Matrix (GLCM) to compose a feature database. Finally, the support vector machine (SVM) and K-Nearest Negbiour (KNN) classifiers are used for classifying the disease. This proposed classifers system is capable to classify the types of blight, brown spot, frogeye leaf spot diseases and Healthy samples with an accuracy of 87.3% and 83.6 % are achieved

Computed tomography guided laser ablation of osteoid osteoma: a study of 30 cases

Background: Osteoid osteoma (OO) is a benign but painful bone lesion that primarily occurs in children and young adults 1. Male:Female ratio is 3:1. The aim of the study was to present our experience of CT guided LASER  ablation  of  radiologicaly proven Osteoid osteomas  in the various bones.Methods: Over the period of 5 years 30 cases of osteoid osteomas in various bones diagnosed on various modalities were treated by CT guided LASER ablation. Bone wise distribution of cases was spine (3), upper end of femur (11), lower end of femur (6), upper end of tibia (4), upper end of humerus (3), lower end of radius (2) and calcaneum (1). 22 patients were treated under spinal and regional anesthesia and 8 patients were treated under short general anesthesia. All the patients were treated on day care basis. The LASER fiber was inserted in the nidus under CT guidance through bone biopsy needle and 1800 joules energy delivered in the lesion continuous mode.Results: 29 (96%) patients have complete relief of pain in twenty-four hours after LASER ablation, One week after treatment all 30 patients were pain free. No neurologic complication was observed in any of our patients with spinal osteoid osteomas.Conclusions: CT guided LASER ablation is a safe, simple and effective method of treatment for osteoid osteoma

Simultaneous estimation of etodolac and thiocolchicoside in bulk and in tablet formulation by UV-spectrophotometry

Two simple, rapid and reproducible simultaneous equation and Q-Analysis UV-Spectrophotometric methods have been developed for simultaneous estimation of Etodolac (ETO) and Thiocolchicoside (THC) in combined tablet dosage form. The methods involved solving simultaneous equations and Q-value Analysis based on measurement of absorbance at wavelengths, 223 (λmax of ETO), 259.4 nm (λmax of THC) and 236 nm (Iso-absorptive point). Linearity was found in the concentration range of 1-6 μg/mL and 4 - 24 μg/mL for ETO & THC respectively with coefficient correlation 0.9998 & 0.9992. The amount of drugs estimated by proposed methods are in excellent agreement with label claimed. Further-more, the methods were applied for the determination of ETO and THC in spiked human urine. The degradation behavior of ETO and THC was investigated under acid hydrolysis, alkali hydrolysis, photo and oxidative degradation. The samples subsequently generated were used for degradation studies using the developed method. Thiocolchicoside was found to degrade extensively under alkali hydrolysis and unaffected by other stress conditions while ETO was found to be stable in all stress conditions. The methods were validated according to ICH guidelines. The method, suitable for routine quality control, has been successfully applied to the determination of both drugs in commercial brands of tablets

Knowledge, attitude and behaviour regarding self-care practices among type 2 diabetes mellitus patients residing in an urban area of South India

Introduction: Diabetes mellitus is a major health problem in India with individual, social and economical consequences. Knowledge, attitude and practice surveys are effective in providing baseline for evaluating intervention programmes. This study was conducted with the aim to know the level of awareness about type 2 diabetes mellitus. Methodology: A cross sectional study conducted to assess the knowledge, attitude and behaviour (KAB) among type 2 diabetes mellitus patients. KAB questionnaire was used to collect data. Results: Out of 1058 patients 992 patients were included for the analysis, rest were excluded due to various reasons. 43.15% were males. Mean age of patients was 55.82 ± 10.2 years. Mean duration of diabetes was 10.2 ± 6.8 years. The mean knowledge score was 4.94, attitude score was 6.29 and behavior score was 1.64. Nearly 38.5% knew definition and types of diabetes. Majority of the participants believed they can control the disease. Dietary modification and exercise among the interviewed subjects was poor.  Conclusion: Results revealed good attitude but poor knowledge and practices (behaviour) towards diabetes. We concluded that there is a need for structured programmes to improve attitude and practices of diabetic patients to promote better compliance towards diet, exercise and drug regimen

Design of electron beam bending magnet system for electron and photon therapy: A simulation approach

The doubly achromatic electron beam bending magnet system using two sector magnets has been designed for the medical applications to treat the cancer. The aim of electron beam bending magnet system is to focus an electron beam having a spot size less than 3 mm × 3 mm, energy spread within 3% and divergence angle ≤ 3mrad at the target position. To achieve these parameters, the simulation has been carried out using Lorentz-3EM software. The beam spot, divergence angle and energy spread have been observed with respect to the variation in angles of sector magnets and drift distance. Based on the simulated results, it has been optimized that the first and second magnet has an angle 206° and 35° and the drift distance 80 mm. It is also observed that at the 1125, 1762, 2570, 3265 and 4155 Amp-turn, the optimized design produces 3369, 4972, 6384, 7584 and 9568 Gauss of magnetic field at median plane which require to bend 6, 9, 12, 15 and 18 MeV energy of electron, respectively, for the electron therapy application. The output beam parameters of the optimized design are energy spread ±3%, divergence angle ~3 mrad and spot size 2.6 mm. Moreover, for 6 MV and 15 MV photon therapy applications, an electron beam of energy 6.5 MeV and 15.5 MeV extracted from magnet system and focused on the bremsstrahlung target. Various materials have been studied for photon generation using Monte Carlo based Fluka code and Tungsten material has been optimized as bremsstrahlung target which produces continuous energy bremsstrahlung spectrum. For the photon therapy, the 1233 and 3327 amp-turn, in an optimized design produces 3616 and 7785 Gauss of magnetic field at median plane require to bend 6.5 and 15.5 MeV energy of electron, respectively, which further produces bremsstrahlung radiation from Tungsten target

An Insight on Analytical Profile on Bisoprolol Fumarate – A Selective Beta-1 Adrenoreceptor Blocker

BF is Beta-adreno receptor antagonist and used as an AntiHypertensive Drug. BF gives the blocking action on β1-adrenergic receptors in the heart and vascular smooth muscle. The present review compiles the various approaches implemented for quantification of BF in bulk drug, pharmaceutical matrix and biological fluid. This review represents more than 50 analytical methods which include capillary electrophoresis, HPLC, HPTLC, UV-Spectroscopy, UPLC, impurity profiling and electrochemical methods implemented for estimation of BF as a single component as well as in multicomponent

Design and evaluation of taste masked chewable dispersible tablet of lamotrigine by melt granulation

Lamotrigine, an antiepileptic drug (AED) of the phenyltriazine class, is chemically unrelated to existing antiepileptic drugs. Lamotrigine is also used in the treatment of depression and bipolar disorder. But it is a bitter drug and slightly soluble in water. Thus, in the work under taken, an attempt was made to mask the taste and to formulate into a chewable dispersible tablet by complexation with Precirol ATO-05, which also acts as taste masking agent. Since, these tablets can be swallowed in the form of dispersion; it is suitable dosage form for paediatric and geriatric patients. Drug-Precirol ATO-05 was prepared in drug to Precirol ATO-05 ratio of 1:2, 1:1.5, 1:1, 1:0.5. The prepared tablets were evaluated for general appearance, content uniformity, hardness, friability, taste evaluation, mouth feel, in vitro disintegration time, and in vitro dissolution studies. Tablets with Precirol ATO-05 have shown good disintegrating features, also, the dispersion not showing any bitter taste, indicate the capability of Precirol ATO-05 used, both as taste masking agents. Almost more than 90 percent of drug was released from the formulation within 1 h. Further formulations were subjected to stability testing for 3 months at temperatures 25±5ºC/60±5%RH; 30±5ºC/65±5%RH and 40±5ºC/75±5%RH. Tablets have shown no appreciable changes with respect to taste, disintegration, and dissolution profiles.Keywords: Lamotrigine; Melt granulation; Precirol; Taste masking; Chewable dispersible tablets