Skip to main content

Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation

In this paper, the authors explore the efficiency of lung segmentation, lossless and lossy data augmentation in  computer-aided diagnosis (CADx) of tuberculosis using deep convolutional neural networks applied to a small and not well-balanced Chest X-ray (CXR) dataset.

Dataset

Shenzhen Hospital (SH) dataset of CXR images was acquired from Shenzhen No. 3 People's Hospital in Shenzhen, China. It contains normal and abnormal CXR images with marks of tuberculosis.

Methodology

Based on previous literature, attempts to perform training for such small CXR datasets without any pre-processing failed to see good results. So the authors attempted segmenting the lung images before being inputted to the model. This gave demonstrated a more successful training and an increase in prediction accuracy.

To perform lung segmentation, i.e. to cut the left and right lung fields from the lung parts in standard CXRs, manually prepared masks were used.

The dataset was split into 8:1:1 parts for training, validation and test parts respectively. Images were rescaled by 1./255 and resized to 2048×2048 and distributed among training, validation and test parts. The model was trained on 
  1. Segmented SH dataset
  2. Segmented SH dataset with lossless data augmentation
  3. Segmented SH dataset with lossy data augmentation

Segmented Dataset

Training on this dataset observed overfitting due to the small size of the dataset. This is reduced by data augmentation methods discussed in the next two sub sections.

Lossless Data Augmentation

The lossless data augmentation for 2D images included the following transformations: mirror-like reflections (left-right and up-down) and rotations by 90n degrees, where n = 1,2,3. This allowed to increase the size of the whole dataset by 8 times obtain the more realistic results on accuracy and loss during training and validation.

Lossy Data Augmentation

Lossy data augmentation for these 2D images included rotations by 5 degrees. This augmentation is in addition to the data augmentation steps taken during lossless data augmentation.

Conclusion

Lossless data augmentation of the segmented dataset leads to the lowest validation loss (without overfitting) and nearly the same accuracy (within the limits of standard deviation) in comparison to the original and other pre-processed datasets after lossy data augmentation. In conclusion, besides the more complex deep CNNs and bigger datasets, the better progress of CADx for the small and not well-balanced datasets even could be obtained by better segmentation, data augmentation, dataset stratification, and exclusion of non-evident outliers.

Comments

Post a Comment

Popular Posts

End-to-End Speech-Driven Facial Animation with Temporal GANs

In this paper , the authors present a system for generating videos of a talking heard, using a still image of a person and an audio clip containing speech. As per the authors this is the first paper that achieves this without any handcrafted features or post-processing of the output. This is achieved using a temporal GAN with 2 discriminators. Novelties in this paper Talking head video generated from still image and speech audio without any subject dependency. Also no handcrafted audio or visual features are used for training and no post-processing of the output (generate facial features from learned metrics). The model captures the dynamics of the entire face producing natural facial expressions such as eyebrow raises, frowns and blinks. This is due to the Recurrent Neural Network ( RNN ) based generator and sequence discriminator. Ablation study to quantify the effect of each component in the system. Image quality is measured using Model Architecture The model consists...
Post a Comment
Read more

Learning to Read Chest X-Rays: Recurrent Neural Feedback Model for Automated Image Annotation

In this paper , the authors present a deep learning model to detect disease from chest x-ray images. A convolutional neural network (CNN) is trained to detect the disease names. Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features. CNN Models used and Dataset CNNs encode input images effectively. In this paper, the authors experiment with a Network in Network (NIN) model and GoogLeNet model. The dataset contains 3,955 radiology reports and 7,470 associated chest x-rays. 71% of the dataset accounts for normal cases (no disease). The data set was balanced by augmenting training images by randomly cropping 224x224 images from the original 256x256 size image. Adaptability of Transfer learning Since this boils down to a classification problem on a small dataset, transfer learning is a technique that comes to our mind. The authors experimented this with ImageNet trained models. ImageNet trained CN...
Post a Comment
Read more

A non-local algorithm for image denoising

Published in   2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, this paper introduces two main ideas Method noise Non-local (NL) means algorithm to denoise images Method noise It is defined as the difference between the original (noisy) image and its denoised version. Some of the intuitions that can be drawn by analysing method noise are Zero method noise means perfect denoising (complete removal of noise without lose of image data). If a denoising method performed well, the method noise must look like a noise and should contain as little structure as possible from the original image The authors then discuss the method noise properties for different denoising filters. They are derived based on the filter properties. We will not be going in detail for each filter as the properties of the filters are known facts. The paper explains those properties using the intuitions of method noise. NL-means idea Denoised value at...
Post a Comment
Read more