scholarly journals Deep convolutional neural networks for COVID‐19 automatic diagnosis

2021 ◽  
Author(s):  
Heba M. Emara ◽  
Mohamed R. Shoaib ◽  
Mohamed Elwekeil ◽  
Walid El‐Shafai ◽  
Taha E. Taha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Automatic Diagnosis

scholarly journals Automatic diagnosis of disc bulge and disc herniation based on lumbar MR images using deep convolutional neural networks (Preprint)

2019 ◽  
Author(s):  
Qiong Pan ◽  
Xiyang Liu ◽  
Kai Zhang ◽  
Lin He ◽  
Zhou Dong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Magnetic Resonance ◽  
Convolutional Neural Networks ◽  
Disc Herniation ◽  
Lumbar Disc ◽  
Diagnostic Efficiency ◽  
Mr Images ◽  
Deep Convolutional Neural Networks ◽  
Automatic Diagnosis ◽  
Disc Bulge

BACKGROUND Lumbar abnormalities often lead to the lower back pain which has keep plaguing people’s lives. Magnetic resonance imaging (MRI) is one of the most efficient techniques to detect lumbar diseases and widely used in clinic. How to interpret massive amounts of magnetic resonance (MR) images quickly and accurately is an urgent problem. OBJECTIVE The aim of this study is to present an automatic system to diagnosis of disc bulge and herniation which is time-saving and effective so that can reduce radiologists’ workload. METHODS The diagnosis of disorders of lumbar vertebral is highly dependent on medical images, therefore, we choose two most common diseases disc bulge and herniation as the research objects. The study is mainly about classification of the axial lumbar disc MR images using deep convolutional neural networks. RESULTS This system comprises of four steps. First step, automatic localizes vertebral bodies (including L1, L2, L3, L4, L5, and S1, L: Lumbar, S: Sacral) in sagittal images using the Faster R-CNN and 4-fold cross-validations show 100% accuracy respectively. Second step, automatically determine the corresponding disc of each axial lumbar disc MR images with 100% accuracy. In the third step, the accuracy to automatic localizes intervertebral disc region of interest (ROI) in axial MR images is 100%. The three classification (disc normal, disc bulge and disc herniation) accuracies in the last step in five groups (L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1) are 92.7%, 84.4%, 92.1%, 90.4% and 84.2% respectively. CONCLUSIONS The automatic diagnosis system was successful built which can classify images of disc normal, disc bulge and disc herniation. This system provides an online test to interpret lumbar disc MR images which will be very helpful in improving the diagnostic efficiency and standardizing diagnosis reports, also, the system can be promoted to detect other lumbar abnormalities and cervical spondylosis.

scholarly journals Spatial Pyramid Pooling in Deep Convolutional Networks for Automatic Tuberculosis Diagnosis

2020 ◽  
Vol 37 (6) ◽  
pp. 1075-1084
Author(s):  
Pike Msonda ◽  
Sait Ali Uymaz ◽  
Seda Soğukpınar Karaağaç
Keyword(s):  
Neural Networks ◽  
Developing Countries ◽  
Convolutional Neural Networks ◽  
Automated System ◽  
Machine Learning Techniques ◽  
World Health ◽  
Deep Convolutional Neural Networks ◽  
Automatic Diagnosis ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

In recent decades, automatic diagnosis using machine-learning techniques have been the focus of research. Mycobacterium Tuberculosis (TB) is a deadly disease that has plagued most developing countries presents a problem that can be tackled by automatic diagnosis. The World Health Organization (WHO) set years 2030 and 2035 as milestones for a significant reduction in new infections and deaths although lack of well-trained professionals and insufficient or fragile public health systems (in developing countries) are just some of the major factors that have slowed the eradication of the TB endemic. Deep convolutional neural networks (DCNNs) have demonstrated remarkable results across problem domains dealing with grid-like data (i.e., images and videos). Traditionally, a methodology for detecting TB is through radiology combined with previous success DCNN have achieved in image classification makes them the perfect candidate to classify Chest X-Ray (CXR) images. In this study, we propose three types of DCNN trained using two public datasets and another new set which we collected from Konya Education and Research Hospital, Konya, Turkey. Also, the DCNN architectures were integrated with an extra layer called Spatial Pyramid Pooling (SPP) a methodology that equips convolutional neural networks with the ability for robust feature pooling by using spatial bins. The result indicates the potential for an automated system to diagnose tuberculosis with accuracies above a radiologist professional.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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