Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.

Hydrothermal synthesis and characterization of carbon-doped TiO2 nanoparticles

We report the hydrothermal growth of pure and doped TiO2 nanoparticles with different concentrations of carbon.The microstructure of the as-synthesized samples is characterized by x-ray diffraction(XRD),field emission scanning electron microscopy(FESEM),energy dispersive x-ray spectroscopy(EDX),and Raman spectroscopy to understand the structure and composition.The XRD patterns confirm the formation of anatase phase of TiO2 with the average crystallite size is calculated to be in the range of 13 nm to 14.7 nm.The functional groups of these nanostructures are characterized by Fourier transformed infrared(FT-IR)spectroscopy,which further confirms the single anatase phase of the synthesized nanostructures.UV-visible absorption spectroscopy is used to understand the absorption behavior,which shows modification in the optical bandgap from 3.13 eV(pure TiO2)to 3.74 eV(1.2 mol%C-doped TiO2).Furthermore,the Ti^3+centers associated with oxygen vacancies are identified using electron paramagnetic resonance spectroscopy(EPR).