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 Ultrasound Breast Cancer Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.
INTRODUCTION: The dataset contains 9,000 images of ultrasound images. The research team collected these images to investigate the machine learning model’s ability to distinguish benign and malignant breast cancer images. The research team also augmented the images by rotating and sharpening them to produce a sufficient amount of images.
ANALYSIS: The Xception model’s performance achieved an accuracy score of 99.57% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 98.33%.
CONCLUSION: In this iteration, the TensorFlow Xception CNN model appeared suitable for modeling this dataset.
Dataset ML Model: Binary classification with numerical features
Dataset Used: Ultrasound Breast Images for Breast Cancer
Dataset Reference: https://www.kaggle.com/datasets/vuppalaadithyasairam/ultrasound-breast-images-for-breast-cancer
One source of potential performance benchmarks: https://www.kaggle.com/datasets/vuppalaadithyasairam/ultrasound-breast-images-for-breast-cancer/code
The HTML formatted report can be found here on GitHub.