This repository hosts a machine learning model designed to assist healthcare professionals in predicting the gender of a fetus from obstetrics ultrasound images. The model, developed as part of a research initiative, aims to serve as a supplementary tool for early, non-invasive fetal assessment, particularly in settings with limited access to specialized medical equipment or expertise.
The core of this project is a deep learning model built with TensorFlow and Keras. The model is based on a Convolutional Neural Network (CNN) architecture, a type of neural network particularly effective for image analysis. By leveraging a diverse dataset of anonymized images, the model learns to identify intricate visual patterns correlated with fetal gender.
Model Architecture: The CNN is constructed with a series of Conv2D and MaxPooling2D layers. The convolutional layers are responsible for feature extraction, while the pooling layers reduce the dimensionality, making the model more robust and efficient.
Prediction Inference: The model provides a probabilistic output (e.g., 95% confidence of female) to give healthcare workers a transparent view of its certainty.
Ethical Considerations: This model is designed with a strong emphasis on responsible AI development in healthcare. The repository includes documentation on data privacy, model limitations, and the ethical guidelines for its use as a decision-support tool.
Technical Stack:
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Frameworks: TensorFlow / Keras
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Language: Python
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Data Handling: NumPy, Pandas