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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.

Impact of Fuzziness for Skin Lesion Classification with Transformer-Based Model

5,500.00
Aim: Ā Ā Ā Ā Ā  The primary objective of this project is to elevate the accuracy of skin lesion classification through the implementation