Showing all 3 results

Automated Brain Tumor Segmentation and Classification in MRI using YOLO-based Deep Learning

5,500.00
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.

Lung Nodule Detection in Medical Images Based on Improved YOLOv5

5,500.00
Aim: To enhance the YOLOv8 model for achieving high-performance object detection in medical imaging and other specialized applications.

Product Recommendation System Using Large Language Model Llama 3

5,500.00
To develop a chatbot that integrates Retrieval-Augmented Generation (RAG) and Llama-3 API for product recommendation by leveraging a vector database with embeddings created using SBERT. This aim involves addressing limitations in traditional recommender systems, such as cold start problems and lack of personalization, by combining state-of-the-art language models with efficient data retrieval mechanisms.