IEEE

An Efficient Ensemble Deep Learning Comparative Study Based on Multi-Class Classifications of Brain Tumors Using Brain MRI Images

Abstract: Brain tumors are a severe health problem that requires accurate diagnosis and classification for optimal treatment. Magnetic resonance imaging (MRI) is a widely used technique for detecting ...
IEEE

Brainai-Net: An Automated Deep Learning Approach For Tumor Detection

Abstract: This study explores the development of a deep learning-based model for the automated detection of brain tumors, aiming to improve diagnostic accuracy and efficiency compared to existing ...
IEEE

Transfer Learning Based Multiclass Brain Tumor Classification Using Mri Data

Abstract: Deep learning for classifying brain tumors has transformed medical image analysis, significantly improving upon manual detection techniques. Brain tumors are defined by aberrant cellular ...
IEEE

Advancements in Plant Disease Detection: A Comparative Study of Deep Learning-Based Classification Models

Abstract: Agriculture is essential to global food security, especially in nations like Bangladesh where it significantly affects people's economies and incomes. However, bacterial, viral, and fungal ...
IEEE

Innovative Lightweight CNN Model for Stage-Wise Alzheimer's Disease Detection

Abstract: A common neurological condition that causes cognitive decline, particularly in older people, is Alzheimer's disease (AD). In order to improve patient treatment and minimize the progression ...
IEEE

A Skin Lesion Classification Approach Using Transfer Learning to Detect Monkeypox

Abstract: Human monkeypox cases have been increasing in recent outbreaks, raising global concern. Historically, the virus was confined to certain regions of Africa. However, in recent times, cases ...
IEEE

An Explainable Approach to Land-Use Classification Using CNN-LSTM and LIME

Abstract: The evolution of landscapes has opened the opportunity to utilize previously unused land for various purposes, such as agriculture and urban development. This approach promotes sustainable ...
IEEE

Deep Learning-Driven Brain Tumor Sectionalisation: Leveraging YOLO11 for MRI Analysis

Abstract: The radiologist has been using magnetic resonance imaging (MRI) because of its exceptional capacity to detect even the smallest abnormalities in the brain. This study contemplates an ...
IEEE

Detection of Blood Clot in Brain Using Supervised Learning Algorithms

Abstract: Ischemic Stroke and its subtypes detection in real time at early stage is a complex task and requires advanced terminologies and tools to learn the statistical pattern form raw data and gets ...
IEEE

ViTBrain: MRI-based Brain Tumour Image detection using Vision Transformer

Abstract: Brain tumors are life-threatening conditions that require early and accurate detection for effective treatment planning. Traditional diagnostic methods, such as MRI scans and biopsies, ...
IEEE

Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models

Abstract: This study uses various models to address network traffic classification, categorizing traffic into web, browsing, IPSec, backup, and email. We collected a comprehensive dataset from Arbor ...
IEEE

A Deep Learning Framework for Efficient Bone Cancer Detection and Classification

Abstract: Deep learning is now a game-changing technology with applications in medical imaging and is especially showing great promise in the diagnosis of malignancies. In summary, bone cancer is a ...