File Structure

.
+-- flow_dataframe.py	
+-- train.py
+-- rsna-bone-age      <-------------- downloaded dataset
|   +-- boneage-test-dataset.csv
|   +-- boneage-train-dataset.csv
|   +-- boneage-train-dataset
|   |   +-- boneage-train-dataset
|   |   |   +-- 1377.png
|   |   |   +-- 1378.png
|   |   |   +-- ...
|   +-- boneage-test-dataset
|   |   +-- boneage-test-dataset
|   |   |   +-- 4360.png
|   |   |   +-- 4361.png
|   |   |   +-- ...

Dataset

The dataset is release to RSNA Pediatric Bone Age Machine Learning Challenge. That is consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set)

Model Structure (16Bit)

Prerequisites

• Python 3.6+
• Tensorflow
• Keras
• Sklearn

Usage

1. Clone this repository.

2. Download images of 2017 RSNA Bone Age Challenge Dataset from this kaggle page and decompress them to the directory. Or download with kaggle-api

   kaggle datasets download -d kmader/rsna-bone-age

3. Setting up your own parameters and run

   python train.py

to-do's

• Adding test.
• Add model's accurancy table.
• Rewrite with pytorch.