Application of Neural Networks for Aiding Diagnosis of Lung Disorders �R Chopade1,A Stanam2, S Pawar3
1Indian Institute of Technology, Kharagpur
2Univeristy of Iowa, Iowa City, Iowa
3Department of Computer Science, Claflin University, Orangeburg, South Carolina
Background
Chest X-rays are currently the best available method for diagnosing different lung associated diseases (Parveen et al., 2011). Chest X-ray exam is one of the most frequent and cost-effective medical imaging examinations. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising works have been reported in the past to achieve a clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult (Speets et al., 2006). With the technology and computing power, the earlier identification of diseases, particularly lung disease can be detected earlier and more accurately, which can save many people as well as reduce the pressure on the system as the health system has not developed in time with the development of the population (Smith-Bindman et al., 2008). We have consistently shown effective usage of supervised and un-supervised machine learning techniques for disease classifications and biomarker predictions (Pawar et al., 2020, 2020, 2021). In this project, we hypothesize the effectiveness of neural networks in prediction (‘Infiltration’, ‘Atelectasis’, ‘Fibrosis’ & ‘Pneumothorax’) and localization ('Cardiomegaly') of specific lung disorders.
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Materials and Methods
Counts per class for primary labels after one-hot encoding.
Results
Model architecture used for all the binary classifiers
Loss vs. learning rate plot for “Pneumothorax” binary classifier trained for 10 epochs
Prediction of location of cardiomegaly with bounding boxes
Accuracy plot for “Pneumothorax” binary classifier with constant learning rate, CLR with “triangular” and CLR with “modified triangular2” policies
Plot showing the "Triangular" policy for “Pneumothorax” binary classifier trained for 36 epochs
Image Label | No. of Images before One Hot Encoding | No. of Image before One Hot Encoding |
No Finding | 60361 | 60361 |
Atelectasis | 4215 | 11559 |
Cardiomegaly | 1093 | 2776 |
Consolidation | 1310 | 4667 |
Edema | 628 | 2303 |
Emphysema | 892 | 2516 |
Effusion | 3955 | 13317 |
Fibrosis | 727 | 1686 |
Infiltration | 9547 | 19894 |
Mass | 2139 | 5782 |
Nodule | 2705 | 6331 |
Pneumothorax | 2194 | 5302 |
Pneumonia | 322 | 1431 |
Pleural Thickening | 1126 | 3385 |
Hernia | 110 | 227 |
Plot for the "modified triangular2" policy of “Pneumothorax” binary classifier trained for 42 epochs