The document outlines a project to develop a machine learning model for x-ray image classification targeting thoracic diseases, utilizing TensorFlow and a large dataset from the NIH Clinical Center. It details the processes of data preparation, including resizing images and converting them into TFRecord format for efficient reading. Future steps involve enhancing the model's architecture with additional layers and data preprocessing techniques.

1. Hariprasad
Radhakrishnan
X-ray Image
ClassificationVersion 0.1
Base Tensorflow
Template

2. Aim
• To build a base template machine learning model capable of X-ray
image classification based on a finite list of thoracic diseases.
• To define and build a base model using Tensorflow and the newly
released tensorflow datasets, TFRecords and features from the latest
release of 1.4.

3. Dataset

4. NIH Clinical Center provides one of the
largest publicly available chest x-ray
datasets to scientific community
A chest x-ray identifies a lung mass.
The recently released dataset has over
100,000 anonymized chest x-ray images
scans from more than 30,000 patients,
including many with advanced lung
disease and their corresponding data to
the scientific community
http://openaccess.thecvf.com/content_cvpr_2
017/papers/Wang_ChestX-ray8_Hospital-
Scale_Chest_CVPR_2017_paper.pdf
Citation:

5. Eight common thoracic diseases
observed in chest X-rays that
validate a challenging task of fully-
automated diagnosis.
Thoracic diseases

6. Data Preparation
TFRecords

7. Image Transformation Label Transformation
To make our computations easier we are resizing our
image from 1024x1024 to 256x256

8. tfrecord
We will convert all our input data into
multiple .tfrecord datasets. TFRecords
are tensorflow input binary files that are
useful when working with large
Datasets.
Instead of storing our annotations
(labels) and images in separate
files/folders and have expensive disk
i/o operations,
We write them together into a few
tfrecord’s for much efficient reading in
the input pipeline of the model.

9. Tensorflow Datasets
We would be using a feature in
Tensorflow called datasets that would
allow us to iteratively process our
multiple input binary files.
A Dataset iterator is useful to get
images and annotations in batches
instead of individual records.
We use a parsing operation to flatten
the image array from 1x256x256 to a
flat 65536.

10. Model Definition
Tensorflow graph

12. We are converting/reshaping a
256x256x1 shaped image to a
flat linear array of 65536
values.

13. Single Image Matrix Multiplication

14. Batch/Multi Image Matrix Multiplication

15. Tensorflow Architecture

16. Tensorflow Architecture

17. TensorBoard

19. Next steps

20. Build upon the based template additional convolutional
layers and experiment with adding contrast to input data.