Python
Advancing Ovarian Cancer Diagnosis Through Deep Learning and Explainable AI: A Multiclassification Approach
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.
Brain Tumor Identification and Classification of MRI images using deep learning techniques
CNN-Keypoint Based Two-Stage Hybrid Approach for Copy-Move Forgery Detection
Coffee Bean Defects Automatic Classification Realtime Application Adopting Deep Learning
Convolution neural network based enhanced computerized Technique for brain tumour detection
Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms
Deep Convolutional Neural Network for Fire Detection
Deep Ensemble Machine for Video Classification
Deep Fake Video Detection Using Transfer learning
