https://github.com/satyanaryana-merla/classifying-anatomical-structure-in-2d-fetal-ultrasound-images

A deep learning model was trained on fetal ultrasound images, achieving promising classification results across four anatomical structures. The model was optimized and tested on a separate dataset. The code organized and saved classified images, establishing a robust pipeline for accurate fetal anatomical structure classification.
https://github.com/satyanaryana-merla/classifying-anatomical-structure-in-2d-fetal-ultrasound-images

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A deep learning model was trained on fetal ultrasound images, achieving promising classification results across four anatomical structures. The model was optimized and tested on a separate dataset. The code organized and saved classified images, establishing a robust pipeline for accurate fetal anatomical structure classification.

• Host: GitHub
• URL: https://github.com/satyanaryana-merla/classifying-anatomical-structure-in-2d-fetal-ultrasound-images
• Owner: Satyanaryana-Merla
• Created: 2023-08-27T05:22:45.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-27T06:03:15.000Z (over 1 year ago)
• Last Synced: 2023-08-27T06:36:27.136Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 6.45 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Classifying-anatomical-structure-in-2D-fetal-ultrasound-images

A deep learning model was trained on fetal ultrasound images, achieving promising classification results across four anatomical structures. The model was optimized and tested on a separate dataset. The code organized and saved classified images, establishing a robust pipeline for accurate fetal anatomical structure classification.

By Satyanarayana Merla

# Due to data privacy, the dataset and data labels are not provided.

## Data Preparation

- The dataset used for this task is loaded from 'E:/task1/image_label.csv'.

- Images were pre-processed, including resizing to 256x256 pixels and normalization.

## Model Training

- A Convolutional Neural Network (CNN) model was used for fetal ultrasound image classification.

- The CNN architecture consisted of multiple convolutional and max-pooling layers followed by fully connected layers.

- Data augmentation techniques were applied to the training set to improve model generalization.

- The model was trained for 10 epochs.

## Model Evaluation

- The trained model was evaluated on a test dataset to assess its performance.

- Evaluation metrics included loss and accuracy.

## Model Performance

- Test Loss: 0.4526

- Test Accuracy: 83.96%

## Model Application

- The trained model was used to classify images into four categories: abdomen, thorax, brain, and femur.

## Classification of External Image

- An external image, 'Patient01600_Plane6_1_of_8.png', was used to test the model's performance.

- The image was classified as: [Insert Predicted Label]

## Future Work

- Potential improvements for model performance were discussed, including transfer learning, hyperparameter tuning, and ensemble methods.

## Conclusion

- In conclusion, the trained model shows promise in classifying fetal ultrasound images into anatomical structures.

- Further improvements and refinements can be explored to enhance its performance.

## Ethical Considerations

- Ethical considerations, such as patient data privacy and responsible AI use, should always be prioritized in medical image analysis.