SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Brain Tumor MRI Images Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Early detection and classification of brain tumors is an important research domain in the field of medical imaging. Detection can help in selecting the most convenient and effective treatment method to save patients’ life. The research team hopes to use Convolutional Neural Network (CNN) based models to classify and detect tumors.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 98.51% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 97.46%.

CONCLUSION: In this iteration, the TensorFlow InceptionV3 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Brain Tumor MRI Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge

One source of potential performance benchmarks: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge/code

The HTML formatted report can be found here on GitHub.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Brain Tumor MRI Images Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Early detection and classification of brain tumors is an important research domain in the field of medical imaging. Detection can help in selecting the most convenient and effective treatment method to save patients’ life. The research team hopes to use Convolutional Neural Network (CNN) based models to classify and detect tumors.

ANALYSIS: The DenseNet201 model’s performance achieved an accuracy score of 97.80% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 96.74%.

CONCLUSION: In this iteration, the TensorFlow DenseNet201 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Brain Tumor MRI Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge

One source of potential performance benchmarks: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge/code

The HTML formatted report can be found here on GitHub.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Brain Tumor MRI Images Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Early detection and classification of brain tumors is an important research domain in the field of medical imaging. Detection can help in selecting the most convenient and effective treatment method to save patients’ life. The research team hopes to use Convolutional Neural Network (CNN) based models to classify and detect tumors.

ANALYSIS: The EfficientNetV2L model’s performance achieved an accuracy score of 96.95% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.65%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2L CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Brain Tumor MRI Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge

One source of potential performance benchmarks: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge/code

The HTML formatted report can be found here on GitHub.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Brain Tumor MRI Images Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Early detection and classification of brain tumors is an important research domain in the field of medical imaging. Detection can help in selecting the most convenient and effective treatment method to save patients’ life. The research team hopes to use Convolutional Neural Network (CNN) based models to classify and detect tumors.

ANALYSIS: The MobileNetV3Large model’s performance achieved an accuracy score of 99.21% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 90.46%.

CONCLUSION: In this iteration, the TensorFlow MobileNetV3Large CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Brain Tumor MRI Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge

One source of potential performance benchmarks: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge/code

The HTML formatted report can be found here on GitHub.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Brain Tumor MRI Images Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Early detection and classification of brain tumors is an important research domain in the field of medical imaging. Detection can help in selecting the most convenient and effective treatment method to save patients’ life. The research team hopes to use Convolutional Neural Network (CNN) based models to classify and detect tumors.

ANALYSIS: The ResNet152V2 model’s performance achieved an accuracy score of 97.76% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 97.83%.

CONCLUSION: In this iteration, the TensorFlow ResNet152V2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Brain Tumor MRI Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge

One source of potential performance benchmarks: https://www.kaggle.com/datasets/mahdinavaei/brain-tumor-mri-images-huge/code

The HTML formatted report can be found here on GitHub.