scholarly journals Computer-aided Detection of Lung Tumors in Chest X-ray Images Using a Bone Suppression Algorithm and A Deep Learning Framework

2021 ◽  
Vol 2071 (1) ◽  
pp. 012002
Author(s):  
K Sato ◽  
N Kanno ◽  
T Ishii ◽  
Y Saijo
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Support Systems ◽  
Early Stage ◽  
Learning Model ◽  
Lung Tumors ◽  
Single Shot ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

Abstract Detecting lung tumors in early stage by reading chest X-ray images is important for radical treatments of the disease. In order to decrease the risk of missed lung tumors, diagnosis support systems that can provide the accurate detection of lung tumors are in high demand, and the use of artificial intelligence with deep learning is one of the promising solutions. In our research, we aim to improve the accuracy of a deep learning-based system for detecting lung tumors by developing a bone suppression algorithm as a preprocessing for the machine-learning model. Our bone suppression algorithm was devised for conventional single-shot chest X-ray images, which do not rely on a specific type of imaging systems. 604 chest X-ray images were processed using the proposed algorithm and evaluated by combining it with a U-net deep learning model. The results showed that the bone suppression algorithm successfully improved the performance of the deep learning model to identify the location of lung tumors (Intersection over Union) from 0.085 (without the bone suppression algorithm) to 0.142, as well as the ability to classify the lung cancer (Area under Curve) that increased from 0.700 to 0.736. The bone suppression algorithm would be useful to improve the accuracy and the reliability of the deep learning-based diagnosis support systems for detecting lung cancer in mass medical examinations.

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

scholarly journals A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images

2021 ◽  
Vol 11 (1) ◽  
pp. 844
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Early Stage ◽  
Diagnostic Technique ◽  
Disease Diagnosis ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Low Sensitivity ◽  
Deep Learning Model

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

scholarly journals Pneumonia detection in chest X-ray images using compound scaled deep learning model

Automatika ◽  
2021 ◽  
Vol 62 (3-4) ◽  
pp. 397-406
Author(s):  
Mohammad Farukh Hashmi ◽  
Satyarth Katiyar ◽  
Abdul Wahab Hashmi ◽  
Avinash G. Keskar
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model
Sign in / Sign up

Export Citation Format

Share Document