3,874 research outputs found
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer
Efficiency of some dimensionality reduction techniques, like lung
segmentation, bone shadow exclusion, and t-distributed stochastic neighbor
embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest
X-ray (CXR) 2D images by deep learning approach to help radiologists identify
marks of lung cancer in CXR. Training and validation of the simple
convolutional neural network (CNN) was performed on the open JSRT dataset
(dataset #01), the JSRT after bone shadow exclusion - BSE-JSRT (dataset #02),
JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation
(dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE
method (dataset #05). The results demonstrate that the pre-processed dataset
obtained after lung segmentation, bone shadow exclusion, and filtering out the
outliers by t-SNE (dataset #05) demonstrates the highest training rate and best
accuracy in comparison to the other pre-processed datasets.Comment: 6 pages, 14 figure
Lung Segmentation from Chest X-rays using Variational Data Imputation
Pulmonary opacification is the inflammation in the lungs caused by many
respiratory ailments, including the novel corona virus disease 2019 (COVID-19).
Chest X-rays (CXRs) with such opacifications render regions of lungs
imperceptible, making it difficult to perform automated image analysis on them.
In this work, we focus on segmenting lungs from such abnormal CXRs as part of a
pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the
high opacity regions as missing data and present a modified CNN-based image
segmentation network that utilizes a deep generative model for data imputation.
We train this model on normal CXRs with extensive data augmentation and
demonstrate the usefulness of this model to extend to cases with extreme
abnormalities.Comment: Accepted to be presented at the first Workshop on the Art of Learning
with Missing Values (Artemiss) hosted by the 37th International Conference on
Machine Learning (ICML). Source code, training data and the trained models
are available here: https://github.com/raghavian/lungVAE
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
Pneumonia is a life-threatening disease, which occurs in the lungs caused by
either bacterial or viral infection. It can be life-endangering if not acted
upon in the right time and thus an early diagnosis of pneumonia is vital. The
aim of this paper is to automatically detect bacterial and viral pneumonia
using digital x-ray images. It provides a detailed report on advances made in
making accurate detection of pneumonia and then presents the methodology
adopted by the authors. Four different pre-trained deep Convolutional Neural
Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for
transfer learning. 5247 Bacterial, viral and normal chest x-rays images
underwent preprocessing techniques and the modified images were trained for the
transfer learning based classification task. In this work, the authors have
reported three schemes of classifications: normal vs pneumonia, bacterial vs
viral pneumonia and normal, bacterial and viral pneumonia. The classification
accuracy of normal and pneumonia images, bacterial and viral pneumonia images,
and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3%
respectively. This is the highest accuracy in any scheme than the accuracies
reported in the literature. Therefore, the proposed study can be useful in
faster-diagnosing pneumonia by the radiologist and can help in the fast airport
screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with
arXiv:2003.1314
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