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TensorFlow Model for Breast Cancer Detection with Data Leakage Analysis

This repository contains a TensorFlow implementation for detecting breast fibroadenoma from ultrasound images, showcasing the journey through data leakage identification to achieving a 43% accuracy rate on the corrected dataset. For those interested in the improved model using PyTorch with an 84% accuracy rate, please visit the PyTorch Model Repository.

Overview

Dataset

The model utilizes a dataset comprising ultrasound images of breast cancer, categorized into benign, malignant, and normal cases:

  • Benign Images: 437
  • Malignant Images: 210
  • Normal Images: 133

Approach

  • Transfer Learning: Applied to leverage knowledge from pre-trained models.
  • Data Augmentation: Included techniques like random affine transformations and color jittering to mitigate the effects of a small and imbalanced dataset.

Data Leakage Analysis

A critical phase in this project involved identifying and rectifying data leakage, which initially led to an inflated accuracy of 89%. The analysis notebook details the steps taken to uncover and correct this issue, providing valuable insights into ensuring the integrity of machine learning models.

TensorFlow Model

After addressing the data leakage, the TensorFlow model's performance settled at 43% accuracy. This section of the repo includes:

  • The model implementation code.
  • The data preparation and augmentation scripts.
  • Hyperparameter tuning and regularization techniques were applied in an attempt to improve performance.

Why 43% Accuracy?

The detailed exploration into the causes of the reduced accuracy post-data leakage correction is documented, along with the limitations encountered during the TensorFlow model's development.

Transition to PyTorch

Encouraged by the need for improved performance, a subsequent model was developed using PyTorch, achieving a remarkable 84% accuracy. For complete details on the PyTorch model, please refer to the PyTorch Model Repository.

Conclusion

This repository not only documents the technical journey of developing a machine learning model for a critical healthcare application but also emphasizes the importance of vigilance against data leakage, illustrating the stark differences in outcomes between TensorFlow and PyTorch implementations.