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 Chest CT-Scan Images Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The research project aims to achieve early detection of lung cancer using CT-Scan images and AI models. The research team deployed deep learning-based classification models in this study to identify potential candidates. For this study, 1,000

ANALYSIS: The DenseNet201 model’s performance achieved an accuracy score of 83.33% after ten epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 81.59%.

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: Chest CT-Scan Images Dataset

Dataset Reference: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images

One source of potential performance benchmarks: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images/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 Chest CT-Scan Images Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The research project aims to achieve early detection of lung cancer using CT-Scan images and AI models. The research team deployed deep learning-based classification models in this study to identify potential candidates. For this study, 1,000

ANALYSIS: The EfficientNetV2L model’s performance achieved an accuracy score of 83.33% after ten epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 78.73%.

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: Chest CT-Scan Images Dataset

Dataset Reference: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images

One source of potential performance benchmarks: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images/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 Chest CT-Scan Images Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The research project aims to achieve early detection of lung cancer using CT-Scan images and AI models. The research team deployed deep learning-based classification models in this study to identify potential candidates. For this study, 1,000 chest CT-Scan images comprise the dataset.

ANALYSIS: The ConvNeXtBase model’s performance achieved an accuracy score of 83.33% after ten epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 72.70%.

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

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Chest CT-Scan Images Dataset

Dataset Reference: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images

One source of potential performance benchmarks: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images/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 Chest CT-Scan Images Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The research project aims to achieve early detection of lung cancer using CT-Scan images and AI models. The research team deployed deep learning-based classification models in this study to identify potential candidates. For this study, 1,000 chest CT-Scan images comprise the dataset.

ANALYSIS: The ResNet152V2 model’s performance achieved an accuracy score of 91.67% after ten epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 77.14%.

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: Chest CT-Scan Images Dataset

Dataset Reference: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images

One source of potential performance benchmarks: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images/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 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.