IEEE

An Evaluation of Medical Image Analysis using Image Segmentation and Deep Learning Techniques for Brain-Tumour

Abstract: An aberrant mass of rapidly proliferating brain cells, known as a brain tumour, may develop into various forms of cancer. The segmentation of brain tumours by MRI scans is a challenging but ...
IEEE

ECHO-SegNet Framework for Accurate 2D Echocardiographic Image Segmentation Using the CAMUS Dataset

Abstract: Accurate segmentation of cardiac structures in echocardiographic images plays a vital role in the quantitative assessment of cardiac function, enabling early detection and management of ...
IEEE

Detection of Bone Cancer using Machine Learning: A Multi-Model Approach

Abstract: Bone cancer diagnosis remains a critical challenge due to the intricate nature of medical imaging data and the need for precise interpretation. This study introduces a machine learning ...
SciELO

Artificial intelligence in the diagnosis and management of dysphagia: a scoping review

Purpose This scoping review aimed to map and synthesize evidence on technological advancements using Artificial Intelligence in the diagnosis and management of dysphagia. We followed the PRISMA ...
IEEE

Brain MRI Analysis Using CNN-Based Feature Extraction and Machine Learning Techniques to Diagnose Alzheimer's Disease

Abstract: This research focuses on developing a complex framework for automatic diagnosis purposes of Alzheimer's disease from MRI brain data, with emphasis placed on advancement in accuracy towards ...
IEEE

3D Reconstruction of Steel Rail using Multi-view Cameras and Mask R-CNN

Abstract: This study proposed a method that integrates multi-view image processing, depth estimation, and point cloud generation to accurately reconstruct a 3D model of a rail. The method is tested by ...
IEEE

A Novel Deep Learning-Based Approach for Brain Tumor Segmentation and Classification Using Diffusion-Weighted Imaging and Magnetic Resonance Spectroscopy

Abstract: Brain tumours (BT) are a dangerous condition that gets a lot of attention. They can be fatal. However, for effective treatment and life-saving measures, early detection and tumour type and ...
IEEE

On Road Vehicle Breakdown Assistance by Using Machine Learning

Abstract: The On Road Vehicle Breakdown Application (ORVBA) functions as an optimal system to resolve remote automotive breakdowns. Primary end-users of the ORVBA platform will use the system to reach ...
IEEE

Physically-Aware Qubit Reuse for Optimized Quantum Circuit Compilation Using Machine Learning

Recently developed quantum computers support mid-circuit measurements, allowing qubit measurement, resetting, and reuse during the execution of quantum circuits. This capability opens new ...
BMJ

Predicting cardiovascular events from routine mammograms using machine learning

10 The George Institute for Global Health, School of Public Health, Imperial College London, London, UK Background Cardiovascular risk is underassessed in women. Many women undergo screening ...
IEEE

Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

Abstract: Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction ...
IEEE

Chronic Kidney Disease Risk Prediction Using Machine Learning

Abstract: Chronic kidney disease is one of the fast-growing global health issues that necessitates early detection for effective management and treatment. This research article examines the ...