A5102821582- Brain Tumor Detection and Classification
- Neural Networks and Applications
- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Particle accelerators and beam dynamics
- Magnetic confinement fusion research
- Cell Image Analysis Techniques
- COVID-19 diagnosis using AI
- Artificial Intelligence in Healthcare and Education
- Smart Agriculture and AI
- Dementia and Cognitive Impairment Research
- Superconducting Materials and Applications
- Machine Learning and ELM
- Artificial Intelligence in Healthcare
- Greenhouse Technology and Climate Control
- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Gyrotron and Vacuum Electronics Research
- Glioma Diagnosis and Treatment
- Medical Imaging and Analysis
Jain University
2024
Institute for Plasma Research
2006
Abstract Breast cancer stands as a paramount public health concern worldwide, underscoring an imperative necessity within the research sphere for precision-driven and efficacious methodologies facilitating accurate detection. The existing diagnostic approaches in breast often suffer from limitations accuracy efficiency, leading to delayed detection subsequent challenges personalized treatment planning. primary focus of this is overcome these shortcomings by harnessing power advanced deep...
Abstract Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis vital for effective treatment planning but often hindered by the complex nature of morphology and variations Traditional methodologies primarily rely on manual interpretation images, supplemented conventional machine learning techniques. These approaches lack robustness scalability needed precise automated classification. The major limitations include high degree...
Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it lengthy process prone to variations different observers. Employing machine learning automate diagnosis of breast presents viable option, striving improve both precision speed. Previous studies have primarily focused on applying various deep models classification images. These methodologies leverage...
Abstract Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of appearances and variations. Traditional methods often require extensive manual intervention are prone human error, leading misdiagnosis delayed treatment. Current approaches primarily include examination by radiologists conventional machine learning techniques. These rely heavily on feature extraction classification algorithms, which may not capture intricate patterns present in brain...
The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes susceptibility to human error.
Classification of different brain tumors is challenging due to unpredictable variations in intra-inter-classes. Unlike existing methods which are not effective for images complex backgrounds, the proposed work aims at accurate classification diverse types such that an appropriate model can be used disease identification. This study considers glioma, meningioma, no tumor, and pituitary classification. To achieve classification, we explore Xception architecture layer, involves flattening,...
<title>Abstract</title> Alzheimer's disease (AD) is a debilitating neurological condition marked by memory loss and an ongoing decrease in cognitive function. Timely management enhanced patient outcomes are contingent upon early precise diagnosis. Deep learning has become effective technique for AD classification. With the use of picture augmentation techniques convolutional neural networks (CNNs), it suggests unique deep framework The methodology provides intuitive visualizations individual...
ABSTRACT Optimizing crop production is essential for sustainable agriculture and food security. This study presents the AgriFusion Model, an advanced ensemble‐based machine learning framework designed to enhance precision by offering highly accurate low‐carbon recommendations. By integrating Random Forest, Gradient Boosting, LightGBM, model combines their strengths boost predictive accuracy, robustness, energy efficiency. Trained on a comprehensive dataset of 2200 instances covering key...