Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
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 Alzheimer’s MRI Images dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: The dataset contains four different classes of Alzheimer’s disease MRI images. The research team collected these images from various websites and manually verified each label. The inspiration behind sharing this dataset is to make highly accurate models for predicting the stages of Alzheimer’s disease.
ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 98.73% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 61.30%.
CONCLUSION: In this iteration, the TensorFlow EfficientNetV2M CNN model appeared suitable for modeling this dataset.
Dataset ML Model: Multi-Class classification with numerical features
Dataset Used: Alzheimer’s Dataset (4 Class of Images)
Dataset Reference: https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images
One source of potential performance benchmarks: https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images/code
The HTML formatted report can be found here on GitHub.