313 research outputs found
Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer
The recent progress of computing, machine learning, and especially deep
learning, for image recognition brings a meaningful effect for automatic
detection of various diseases from chest X-ray images (CXRs). Here efficiency
of lung segmentation and bone shadow exclusion techniques is demonstrated for
analysis of 2D CXRs by deep learning approach to help radiologists identify
suspicious lesions and nodules in lung cancer patients. Training and validation
was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset,
i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02),
original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset
after segmentation (dataset #04). The results demonstrate the high efficiency
and usefulness of the considered pre-processing techniques in the simplified
configuration even. The pre-processed dataset without bones (dataset #02)
demonstrates the much better accuracy and loss results in comparison to the
other pre-processed datasets after lung segmentation (datasets #02 and #03).Comment: 10 pages, 7 figures; The First International Conference on Computer
Science, Engineering and Education Applications (ICCSEEA2018)
(www.uacnconf.org/iccseea2018) (accepted
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
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
Lung cancer is the leading cause of cancer death and early diagnosis is
associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive
imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to
distinguish from vascular and bone structures using CXR. Computer vision has
previously been proposed to assist human radiologists in this task, however,
leading studies use down-sampled images and computationally expensive methods
with unproven generalization. Instead, this study localizes lung nodules using
efficient encoder-decoder neural networks that process full resolution images
to avoid any signal loss resulting from down-sampling. Encoder-decoder networks
are trained and tested using the JSRT lung nodule dataset. The networks are
used to localize lung nodules from an independent external CXR dataset.
Sensitivity and false positive rates are measured using an automated framework
to eliminate any observer subjectivity. These experiments allow for the
determination of the optimal network depth, image resolution and pre-processing
pipeline for generalized lung nodule localization. We find that nodule
localization is influenced by subtlety, with more subtle nodules being detected
in earlier training epochs. Therefore, we propose a novel self-ensemble model
from three consecutive epochs centered on the validation optimum. This ensemble
achieved a sensitivity of 85% in 10-fold internal testing with false positives
of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6
following morphological false positive reduction. This result is comparable to
more computationally complex systems based on linear and spatial filtering, but
with a sub-second inference time that is faster than other methods. The
proposed algorithm achieved excellent generalization results against an
external dataset with sensitivity of 77% at a false positive rate of 7.6
DETECTION OF PNEUMONIA BY USING NINE PRE-TRAINED TRANSFER LEARNING MODELS BASED ON DEEP LEARNING TECHNIQUES
Pneumonia is a serious chest disease that affects the lungs. This disease has become an important issue that must be taken care of in the field of medicine due to its rapid and intense spread, especially among people who are addicted to smoking. This paper presents an efficient prediction system for detecting pneumonia using nine pre-trained transfer learning models based on deep learning technique (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, and SqueezeNet). The dataset in this study consisted of 5856 chest x-rays, which were divided into 5216 for training and 624 for the test. In the training phase, the images were pre-processed by resizing the input images to the same dimensions to reduce complexity and computation. The images are then forwarded to the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, SqueezeNet) to extract features and classify the images as normal or pneumonia. The results of the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121 DenseNet-169, AlexNet and SqueezeNet) give accuracies (98.72%, 98.94%, 98.88%, 98.72%, 96.2%, 94.69%, 96.29%, 95.01% and 96.10%) respectively. We found that the SeNet-154 model gave the best result with an accuracy of 98.94% with a validation loss (0.018103). When comparing our results with older studies, it should be noted that the proposed method is superior to other methods
Classification of lung diseases using deep learning models
Although deep learning-based models show high performance in the medical field, they required large volumes of data which is problematic due to the protection of patient privacy and lack of publically available medical databases.
In this thesis, we address the problem of medical data scarcity by considering the task of pulmonary disease detection in chest X-Ray images using small volume datasets (<1000 samples). We implement three deep convolution neural networks pre-trained on the ImageNet dataset (VGG16, ResNet-50, and InveptionV3) and asses them in the lung disease classification tasks transfer learning approach. We created a pipeline that applied segmentation on Chest X-Ray images before classifying them and we compared the performance of our framework with the existing one. We demonstrated that pre-trained models and simple classifiers such as shallow neural networks can compete with the complex systems.
We also implemented activation maps for our system. The analysis of class activation maps shows that not only does the segmentation improve results in terms of accuracy but also focuses models on medically relevant areas of lungs.
We validated our techniques on the publicly available Shenzhen and Montgomery datasets and compared them to the currently available solutions. Our method was able to reach the same level of accuracy as the best performing models trained on the Montgomery dataset however, the advantage of our approach is a smaller number of trainable parameters. What is more, our InceptionV3 based model almost tied with the best performing solution on the Shenzhen dataset but as previously, it is computationally less expensive
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