4,138 research outputs found
Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review
Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy
Revisiting Computer-Aided Tuberculosis Diagnosis
Tuberculosis (TB) is a major global health threat, causing millions of deaths
annually. Although early diagnosis and treatment can greatly improve the
chances of survival, it remains a major challenge, especially in developing
countries. Recently, computer-aided tuberculosis diagnosis (CTD) using deep
learning has shown promise, but progress is hindered by limited training data.
To address this, we establish a large-scale dataset, namely the Tuberculosis
X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with
corresponding bounding box annotations for TB areas. This dataset enables the
training of sophisticated detectors for high-quality CTD. Furthermore, we
propose a strong baseline, SymFormer, for simultaneous CXR image classification
and TB infection area detection. SymFormer incorporates Symmetric Search
Attention (SymAttention) to tackle the bilateral symmetry property of CXR
images for learning discriminative features. Since CXR images may not strictly
adhere to the bilateral symmetry property, we also propose Symmetric Positional
Encoding (SPE) to facilitate SymAttention through feature recalibration. To
promote future research on CTD, we build a benchmark by introducing evaluation
metrics, evaluating baseline models reformed from existing detectors, and
running an online challenge. Experiments show that SymFormer achieves
state-of-the-art performance on the TBX11K dataset. The data, code, and models
will be released.Comment: 14 page
PadChest: A large chest x-ray image dataset with multi-label annotated reports
We present a labeled large-scale, high resolution chest x-ray dataset for the
automated exploration of medical images along with their associated reports.
This dataset includes more than 160,000 images obtained from 67,000 patients
that were interpreted and reported by radiologists at Hospital San Juan
Hospital (Spain) from 2009 to 2017, covering six different position views and
additional information on image acquisition and patient demography. The reports
were labeled with 174 different radiographic findings, 19 differential
diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and
mapped onto standard Unified Medical Language System (UMLS) terminology. Of
these reports, 27% were manually annotated by trained physicians and the
remaining set was labeled using a supervised method based on a recurrent neural
network with attention mechanisms. The labels generated were then validated in
an independent test set achieving a 0.93 Micro-F1 score. To the best of our
knowledge, this is one of the largest public chest x-ray database suitable for
training supervised models concerning radiographs, and the first to contain
radiographic reports in Spanish. The PadChest dataset can be downloaded from
http://bimcv.cipf.es/bimcv-projects/padchest/
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
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