152 research outputs found
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
Image Features for Tuberculosis Classification in Digital Chest Radiographs
Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very large, image features are used to describe the CXR image in a lower dimension while preserving the elements in the CXR necessary for the detection of TB. In this thesis, I present a set of image features using Pyramid Histogram of Oriented Gradients, Local Binary Patterns, and Principal Component Analysis which provides high classifier performance on two publicly available CXR datasets, and compare my results to current state-of-the-art research
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
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
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases
Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI
Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study
Lung segmentation in chest X-rays (CXRs) is an important prerequisite for
improving the specificity of diagnoses of cardiopulmonary diseases in a
clinical decision support system. Current deep learning (DL) models for lung
segmentation are trained and evaluated on CXR datasets in which the
radiographic projections are captured predominantly from the adult population.
However, the shape of the lungs is reported to be significantly different for
pediatrics across the developmental stages from infancy to adulthood. This
might result in age-related data domain shifts that would adversely impact lung
segmentation performance when the models trained on the adult population are
deployed for pediatric lung segmentation. In this work, our goal is to analyze
the generalizability of deep adult lung segmentation models to the pediatric
population and improve performance through a systematic combinatorial approach
consisting of CXR modality-specific weight initializations, stacked
generalization, and an ensemble of the stacked generalization models. Novel
evaluation metrics consisting of Mean Lung Contour Distance and Average Hash
Score are proposed in addition to the Multi-scale Structural Similarity Index
Measure, Intersection of Union, and Dice metrics to evaluate segmentation
performance. We observed a significant improvement (p < 0.05) in cross-domain
generalization through our combinatorial approach. This study could serve as a
paradigm to analyze the cross-domain generalizability of deep segmentation
models for other medical imaging modalities and applications.Comment: 11 pages, 7 figures, and 8 table
Deep Learning Models for Classification of Lung Diseases
This thesis focuses on the importance of early detection in lung cancer through the use of medical imaging techniques and deep learning models. The current practice of examining nodules larger than 7 mm can delay detection and allow cancerous nodules to grow undetected. The project aims to detect nodules as small as 3 mm to improve the chances of early cancer identification. The use of constrained volume datasets and transfer learning techniques addresses the scarcity of medical data, and deep neural networks are employed for classification and segmentation tasks. Despite the limited dataset, the results demonstrate the effectiveness of the proposed models. Class activation maps and segmentation techniques enhance accuracy and provide insights into the most critical areas for diagnosis. This research contributes to the understanding of lung disease diagnosis and highlights the potential of deep learning in medical imaging. 
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