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Detection of tuberculosis from chest X-ray images: Boosting the performance with vision transformer and transfer learning
تشخیص سل از تصاویر اشعه ایکس قفسه سینه: افزایش عملکرد با ترانسفورماتور بینایی و انتقال یادگیری-2021 Tuberculosis (TB) caused by Mycobacterium tuberculosis is a contagious disease which is among the top deadly diseases in the world. Research in Medical Imaging has been done to provide doctors with techniques and tools to early detect, monitor and diagnose the disease using Artificial Intelligence. Recently, many attempts have been made to automatically recognize TB from chest X-ray (CXR) images. Still, while the obtained performance is encouraging, according to our investigation, many of the existing approaches have been evaluated on small and undiverse datasets. We suppose that such a good performance might not hold for heterogeneous data sources, which originate from real world scenarios. Our present work aims to fill the gap and improve the prediction performance on larger datasets. In particular, we present a practical solution for the detection of tuberculosis from CXR images, making use of cutting-edge Machine Learning and Computer Vision algorithms. We conceptualize a framework by adopting three recent deep neural networks as the main classification engines, namely modified EfficientNet, modified original Vision Transformer, and modified Hybrid EfficientNet with Vision Transformer. Moreover, we also empower the learning process with various augmentation techniques. We evaluated the proposed approach using a large dataset which has been curated by merging various public datasets. The resulting dataset has been split into training, validation, and testing sets which account for 80%, 10%, and 10% of the original dataset, respectively. To further study our proposed approach, we compared it with two state-of-the-art systems. The obtained results are encouraging: the maximum accuracy of 97.72% with AUC of 100% is achieved with ViT_Base_EfficientNet_B1_224. The experimental results demonstrate that our conceived tool outperforms the considered baselines with respect to different quality metrics. Keywords: Deep learning | EfficientNet | Tuberculosis detection | Transfer learning | Transformer |
مقاله انگلیسی |
2 |
Effectiveness assessing of softwares with AI for chest area x-ray images post-processing
ارزیابی اثربخشی نرم افزارها با هوش مصنوعی برای تصاویر اشعه ایکس ناحیه قفسه سینه پس از پردازش-2020 Diagnostic radiology is a branch of medicine
describes of ionizing radiation using to study the structure and
functions of normal and pathological altered human organs
and systems for the prevention and detection of diseases. X-ray
irradiation is the most common diagnostic method in
Radiology and it makes possible to identify and diagnose
diseases and injuries for further treatment of patients.
In particular, development of artificial intelligence (AI) has
led the creation of different software for X-ray images
processing to improve pathologists recognition by operator. At
this moment there are lots of means and methods have been
developed by different software engineers to find out "the
perfect solution" for this problem.
Respiratory diseases occupy one of the leading places in
Ukraine. The main share of pathologies is acute viral
infections, bronchitis, pneumonia and tuberculosis. In one
year, about 15.5 million chest X-ray studies perform in
Ukraine only for tuberculosis detection. It was the reason to
select chest X-ray studies for processing’s effectiveness
assessing.
For effectiveness assessing we have made quantitative
contrast measurements for selected areas on original and
processed chest X-ray images. Keywords: X-ray | tomosynthesis | digital radiology | contrast deviations | AI |
مقاله انگلیسی |