• Unauthorized viewing and diagnosis of sensitive anatomical features in ultrasound images, such as fetal gender, is a challenge, particularly in regions with regulations against gender identification.
• This invention addresses the need for an automated system to filter out and prevent access to restricted anatomical features in ultrasound scans, ensuring compliance with regulations and privacy protection.

Deep Learning:

• Utilizes deep neural networks, specifically Convolutional Neural Networks (CNNs), to process and analyze ultrasound images.

Real-time Image Processing:

• Performs automated filtering of sensitive anatomical features in ultrasound images in real-time, ensuring immediate compliance with privacy regulations.

Data Pre-processing:

• Pre-processes image data using a neural network architecture to identify and filter out salient anatomical features, such as fetal gender indicators.

Network Training:

• Trains the deep-learning network with a balanced dataset of data classes, enabling accurate and reliable detection and filtering of anatomical features.

Integration:

• Can be integrated with ultrasound machines and healthcare systems to enhance privacy protection and regulatory compliance in medical imaging.