Brain Tumor Classification Using EfficientNet B3
Budget: ₹750 – ₹1,250 INR
I'm working on a project involving the engineering of a brain tumor classification model using EfficientNet B3. The model has shown impressive results, achieving a training accuracy of 99% and a validation accuracy of 98% in just 15 epochs on MRI datasets.
Key tasks:
- Utilize Transfer Learning and fine-tune the model with Adaptive Learning Rate Scheduling and Regularization Techniques to ensure high performance with minimal overfitting.
- Implement advanced image preprocessing techniques such as Contrast Adjustment, Noise Reduction, and Data Augmentation to enhance feature extraction and improve generalization.
- Integrate Grad-CAM visualizations to enhance model transparency and use precision-driven metrics like AUC-ROC, F1-Score, and Precision-Recall Curves for performance evaluation.
- Deploy the trained model via Flask and Streamlit to provide a user-friendly web interface for real-time tumor classification, ultimately enhancing clinical usability and accessibility.
The primary objective of this model is for research purposes, and it was trained using a Kaggle dataset. The specific MRI sequences included in the dataset have not been stated.
Ideal candidates for this project should be proficient in machine learning and have experience with EfficientNet, MRI datasets, and the mentioned deployment tools.
Key tasks:
- Utilize Transfer Learning and fine-tune the model with Adaptive Learning Rate Scheduling and Regularization Techniques to ensure high performance with minimal overfitting.
- Implement advanced image preprocessing techniques such as Contrast Adjustment, Noise Reduction, and Data Augmentation to enhance feature extraction and improve generalization.
- Integrate Grad-CAM visualizations to enhance model transparency and use precision-driven metrics like AUC-ROC, F1-Score, and Precision-Recall Curves for performance evaluation.
- Deploy the trained model via Flask and Streamlit to provide a user-friendly web interface for real-time tumor classification, ultimately enhancing clinical usability and accessibility.
The primary objective of this model is for research purposes, and it was trained using a Kaggle dataset. The specific MRI sequences included in the dataset have not been stated.
Ideal candidates for this project should be proficient in machine learning and have experience with EfficientNet, MRI datasets, and the mentioned deployment tools.
Related categories:
Python
Matlab and Mathematica
Machine Learning (ML)
Data Analytics
Deep Learning