DeepChest - Efficient Deep Learning Framework for Detection of Chest Pathologies using Chest X-ray Images

Dataset

The project uses the NIH Chest X-ray Dataset here is the here to acces it. This dataset has 112k images of Chest Xrays and these include the following diseases.

• Atelectasis
• Consolidation
• Infiltration
• Pneumothorax
• Edema
• Emphysema
• Fibrosis
• Effusion
• Pneumonia
• Pleural_thickening
• Cardiomegaly
• Nodule Mass
• Hernia

Problem Statement

Through this project, we aim to enable low-power portable healthcare diagnostic support solutions. We explore Binarized Neural Networks (BNN) for the efficient diagnosis of thoracic pathologies via Chest X-Ray images. We test our model on the publicly available NIH Chest X-Ray dataset and achieve state-of-the-art results while consuming substantially less resources than the current state-of-the-art network, CheXNet.

Pretrained Weights

Binary and Full Precision weights can be found here

Samples

Label: Cardiomegaly
Model Output: Cardiomegaly

Label: Cardiomegaly and Emphysema
Model Output: Cardiomegaly and Emphysema

Label: No Finding
Model Output: No Finding

How to reproduce results?

• Place all the images in data folder
• Place train.csv and dev.csv in the same folder
• Run the following command python test.py