Work Collection
Work Collection
Work Collection
Project Page
Brain Tumor Detection
Project Page
Overview
Overview
Overview
Sed ut perspiciatis unde omnis iste natus error sit voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit.
Approach
Approach
Approach
Nam libero tempore, cum soluta nobis est eligendi optio cumque nihil impedit quo minus id quod maxime placeat. Nihil molestiae consequatur, vel illum qui dolorem eum. Architecto beatae vitae dicta sunt explicabo.
Brain Tumor Detection Using Vision Transformers
Brain Tumor Detection Using
Vision Transformers
Brain Tumour are one of the most critical medical conditions, requiring precise and early detection to improve treatment outcomes. Leveraging cutting-edge deep learning technologies, Brain TumourI developed a robust brain tumor detection system using Vision Transformers (ViT). This project is a testament to my expertise in applying state-of-the-art machine learning techniques to solve complex real-world problems, seamlessly integrating advanced AI solutions into healthcare.
Brain Tumour are one of the most critical medical conditions, requiring precise and early detection to improve treatment outcomes. Leveraging cutting-edge deep learning technologies, Brain TumourI developed a robust brain tumor detection system using Vision Transformers (ViT). This project is a testament to my expertise in applying state-of-the-art machine learning techniques to solve complex real-world problems, seamlessly integrating advanced AI solutions into healthcare.
Objective
Objective
The primary goal of this project was to design a highly accurate and efficient system for detecting brain tumors from MRI scans. By utilizing Brain Tumour Vision Transformers, the model captures intricate details and spatial relationships in MRI images, enabling superior performance compared to traditional CNN-based models.
The primary goal of this project was to design a highly accurate and efficient system for detecting brain tumors from MRI scans. By utilizing Brain Tumour Vision Transformers, the model captures intricate details and spatial relationships in MRI images, enabling superior performance compared to traditional CNN-based models.
How I did it
How I did it
How I did it
Model Architecture: Vision Transformers (ViT), known for their ability to handle large-scale image data and capture global dependencies efficiently.
Dataset: The project utilized the BraTS dataset, a well-known repository containing multi-modal MRI scans, including T1-weighted and T2-weighted images.
Preprocessing: Implemented advanced image preprocessing techniques, including normalization and augmentation, to enhance model generalization.
Performance Metrics: Achieved an accuracy of 97.8% with a recall of 98.5%, demonstrating the model's reliability in detecting even subtle abnormalities.
Model Architecture: Vision Transformers (ViT), known for their ability to handle large-scale image data and capture global dependencies efficiently.
Dataset: The project utilized the BraTS dataset, a well-known repository containing multi-modal MRI scans, including T1-weighted and T2-weighted images.
Preprocessing: Implemented advanced image preprocessing techniques, including normalization and augmentation, to enhance model generalization.
Performance Metrics: Achieved an accuracy of 97.8% with a recall of 98.5%, demonstrating the model's reliability in detecting even subtle abnormalities.
Methodology
Data Preparation:
Preprocessed MRI scans from the BraTS dataset, including resizing, normalization, and augmentation to ensure robustness against noise and artifacts.
Handled class imbalances by using weighted loss functions and oversampling techniques.
Model Design:
Customized a Vision Transformer architecture tailored for high-resolution medical images.
Integrated attention mechanisms to highlight critical areas in MRI scans.
Fine-tuned the model using transfer learning to leverage pre-trained weights on large-scale datasets.
Evaluation:
Performed rigorous testing using cross-validation and external test sets.
Compared the model's performance with existing CNN-based approaches, achieving significant improvements in accuracy and recall.
Methodology
Data Preparation:
Preprocessed MRI scans from the BraTS dataset, including resizing, normalization, and augmentation to ensure robustness against noise and artifacts.
Handled class imbalances by using weighted loss functions and oversampling techniques.
Model Design:
Customized a Vision Transformer architecture tailored for high-resolution medical images.
Integrated attention mechanisms to highlight critical areas in MRI scans.
Fine-tuned the model using transfer learning to leverage pre-trained weights on large-scale datasets.
Evaluation:
Performed rigorous testing using cross-validation and external test sets.
Compared the model's performance with existing CNN-based approaches, achieving significant improvements in accuracy and recall.
Key Contributions
Innovative Use of Transformers: This project demonstrates my ability to apply Vision Transformers to a specialized domain, highlighting my versatility and forward-thinking approach.
Advanced Problem Solving: Tackled challenges such as class imbalance, memory optimization for large MRI datasets, and maintaining computational efficiency.
Integration with Real-World Applications: Developed a user-friendly web application powered by MongoDB for storing and visualizing patient data. This end-to-end solution showcases my capability to translate AI research into impactful tools for healthcare professionals.
Key Contributions
Innovative Use of Transformers: This project demonstrates my ability to apply Vision Transformers to a specialized domain, highlighting my versatility and forward-thinking approach.
Advanced Problem Solving: Tackled challenges such as class imbalance, memory optimization for large MRI datasets, and maintaining computational efficiency.
Integration with Real-World Applications: Developed a user-friendly web application powered by MongoDB for storing and visualizing patient data. This end-to-end solution showcases my capability to translate AI research into impactful tools for healthcare professionals.
hi gpwob,hbklhb,jdxhb,wjshdx,bjsa
Framer 2023
Amsterdam