Python
ATT Squeeze U-Net A Lightweight Network for Forest Fire Detection and Recognition
Automated Brain Tumor Segmentation and Classification in MRI using YOLO-based Deep Learning
Python, Deep Learning, Generative AI, Projects, Artificial Intelligence, Deep Learning, Generative AI
The aim of this research is to develop a more effective and efficient brain tumor segmentation system using the YOLOv11 architecture. The focus is on enhancing the accuracy and reliability of tumor identification in brain imaging, specifically through advanced segmentation techniques. By leveraging deep learning models, the study seeks to provide an automated solution for real-time tumor segmentation, assisting in clinical decision-making and early diagnosis.
Automated Smart Attendance System Using Face Recognition
Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy
BGL-PhishNet: Phishing Website Detection Using Hybrid Model-BERT, GNN, and LightGBM
Blockchain and AI-Empowered Healthcare Insurance Fraud Detection: An Analysis, Architecture, and Future Prospects
BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features
Brain Tumor Detection and Classification Using Intelligence Techniques An Overview
Brain Tumor Identification and Classification of MRI images using deep learning techniques
