scholarly journals A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

2019 ◽  
pp. 102-112
Author(s):  
J. Juditha Mercina ◽  
J. Madhumathi ◽  
V. Priyanga ◽  
M. Deva Priya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Lung Disease ◽  
Lung Diseases ◽  
Intelligent System ◽  
Lung Disorder ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Lungs play an important role in human respiratory system. There are diseases that affect the functioning of lungs. To analyse lung diseases in the chest region using X-ray based Computer-Aided Diagnosis (CAD) system, it is necessary to determine the lung regions subject to analysis. In this paper, an intelligent system is proposed for lung disease detection. In this paper, Interstitial Lung Disease (ILD) patterns are classified using Convolutional Neural Networks (CNN). The proposed system involves five convolutional layers and three dense layers. The performance of the classification demonstrates the potential of CNN in analysing lung patterns.

scholarly journals Fast Deep Learning Computer-Aided Diagnosis against the Novel COVID-19 pandemic from Digital Chest X-ray Images

2020 ◽  
Author(s):  
Mugahed A. Al-antari ◽  
Cam-Hao Hua ◽  
Sungyoung Lee
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Lung Diseases ◽  
Diagnostic Performance ◽  
The Novel ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Chest X Ray ◽  
Aided Diagnosis

Abstract Background and Objective: The novel coronavirus 2019 (COVID-19) is a harmful lung disease that rapidly attacks people worldwide. At the end of 2019, COVID-19 was discovered as mysterious lung disease in Wuhan, Hubei province of China. World health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 from the entire digital X-ray images is the key to efficiently assist patients and physicians for a fast and accurate diagnosis.Methods: In this paper, a deep learning computer-aided diagnosis (CAD) based on the YOLO predictor is proposed to simultaneously detect and diagnose COVID-19 among the other eight lung diseases: Atelectasis, Infiltration, Pneumothorax, Mass, Effusion, Pneumonia, Cardiomegaly, and Nodule. The proposed CAD system is assessed via five-fold tests for multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system is trained using an annotated training set of 50,490 chest X-ray images.Results: The suspicious regions of COVID-19 from the entire X-ray images are simultaneously detected and classified end-to-end via the proposed CAD predictor achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. The most testing images of COVID-19 and other lunge diseases are correctly predicted achieving intersection over union (IoU) with their GTs greater than 90%. Applying deep learning regularizers of data balancing and augmentation improve the diagnostic performance by 6.64% and 12.17% in terms of overall accuracy and F1-score, respectively. Meanwhile, the proposed CAD system presents its feasibility to diagnose the individual chest X-ray image within 0.009 second. Thus, the presented CAD system could predict 108 frames/second (FPS) at the real-time of prediction.Conclusion: The proposed deep learning CAD system shows its capability and reliability to achieve promising COVID-19 diagnostic performance among all other lung diseases. The proposed deep learning model seems reliable to assist health care systems, patients, and physicians in their practical validations.

scholarly journals Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans

2020 ◽  
Author(s):  
Mohamed Esmail Karar ◽  
Ezz El-Din Hemdan ◽  
Marwa A. Shouman
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Detection Accuracy ◽  
Cad Systems ◽  
X Ray ◽  
Learning Framework ◽  
Learning Classifiers ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract Computer-aided diagnosis (CAD) systems are considered a powerful tool for physicians to support identification of the novel Coronavirus Disease 2019 (COVID-19) using medical imaging modalities. Therefore, this article proposes a new framework of cascaded deep learning classifiers to enhance the performance of these CAD systems for highly suspected COVID-19 and pneumonia diseases in X-ray images. Our proposed deep learning framework constitutes two major advancements as follows. First, complicated multi-label classification of X-ray images have been simplified using a series of binary classifiers for each tested case of the health status. That mimics the clinical situation to diagnose potential diseases for a patient. Second, the cascaded architecture of COVID-19 and pneumonia classifiers is flexible to use different fine-tuned deep learning models simultaneously, achieving the best performance of confirming infected cases. This study includes eleven pre-trained convolutional neural network models, such as Visual Geometry Group Network (VGG) and Residual Neural Network (ResNet). They have been successfully tested and evaluated on public X-ray image dataset for normal and three diseased cases. The results of proposed cascaded classifiers showed that VGG16, ResNet50V2, and Dense Neural Network (DenseNet169) models achieved the best detection accuracy of COVID-19, viral (Non-COVID-19) pneumonia, and bacterial pneumonia images, respectively. Furthermore, the performance of our cascaded deep learning classifiers is superior to other multi-label classification methods of COVID-19 and pneumonia diseases in previous studies. Therefore, the proposed deep learning framework presents a good option to be applied in the clinical routine to assist the diagnostic procedures of COVID-19 infection.

Computer-aided Diagnosis with a Convolutional Neural Network Algorithm for Automated Detection of Urinary Tract Stones on Plain X-ray

2021 ◽  
Author(s):  
Masaki Kobayashi ◽  
Junichiro Ishioka ◽  
Yoh Matsuoka ◽  
Yuichi Fukuda ◽  
Yusuke Kohno ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Urinary Tract ◽  
Convolutional Neural Network ◽  
Computer Aided Diagnosis ◽  
Cad Model ◽  
X Ray ◽  
Computer Aided ◽  
Urinary Tract Stones ◽  
Aided Diagnosis

Abstract Background: Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis using this method is not always easy. Since the advent of a convolutional neural network via deep learning in the 2000s, computer-aided diagnosis (CAD) has had a great impact on automatic image analysis in the urological field. The objective of our study was to develop a CAD system with deep learning architecture to detect urinary tract stones on a plain X-ray and to evaluate the model’s accuracy. Methods: We collected plain X-ray images of 1017 patients with a radio-opaque urinary tract stone. X-ray images (n=827 and 190) were used as the training and test data, respectively. We used a 17-layer Residual Network as a convolutional neural network architecture for patch-wise training. The training data were repeatedly used until the best model accuracy was achieved within 300 runs. The F score, which is a harmonic mean of the sensitivity and positive predictive value (PPV) and represents the balance of the accuracy, was measured to evaluate the model’s accuracy. Results: Using deep learning, we developed a CAD model that needed 110 ms to provide an answer for each X-ray image. The best F score was 0.752, and the sensitivity and PPV were 0.872 and 0.662, respectively. When limited to a proximal ureter stone, the sensitivity and PPV were 0.925 and 0.876, respectively, and they were the lowest at mid-ureter. Conclusion: CAD of a plain X-ray may be a promising method to detect radio-opaque urinary tract stones with satisfactory sensitivity although the PPV could still be improved. The CAD model detects urinary tract stones quickly and automatically and has the potential to become a helpful screening modality especially for primary care physicians for diagnosing urolithiasis. Further study using a higher volume of data would improve the diagnostic performance of CAD models to detect urinary tract stones on a plain X-ray.

scholarly journals Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Masaki Kobayashi ◽  
Junichiro Ishioka ◽  
Yoh Matsuoka ◽  
Yuichi Fukuda ◽  
Yusuke Kohno ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Urinary Tract ◽  
Convolutional Neural Network ◽  
Computer Aided Diagnosis ◽  
Cad Model ◽  
X Ray ◽  
Computer Aided ◽  
Urinary Tract Stones ◽  
Aided Diagnosis

Abstract Background Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis using this method is not always easy. Since the advent of a convolutional neural network via deep learning in the 2000s, computer-aided diagnosis (CAD) has had a great impact on automatic image analysis in the urological field. The objective of our study was to develop a CAD system with deep learning architecture to detect urinary tract stones on a plain X-ray and to evaluate the model’s accuracy. Methods We collected plain X-ray images of 1017 patients with a radio-opaque upper urinary tract stone. X-ray images (n = 827 and 190) were used as the training and test data, respectively. We used a 17-layer Residual Network as a convolutional neural network architecture for patch-wise training. The training data were repeatedly used until the best model accuracy was achieved within 300 runs. The F score, which is a harmonic mean of the sensitivity and positive predictive value (PPV) and represents the balance of the accuracy, was measured to evaluate the model’s accuracy. Results Using deep learning, we developed a CAD model that needed 110 ms to provide an answer for each X-ray image. The best F score was 0.752, and the sensitivity and PPV were 0.872 and 0.662, respectively. When limited to a proximal ureter stone, the sensitivity and PPV were 0.925 and 0.876, respectively, and they were the lowest at mid-ureter. Conclusion CAD of a plain X-ray may be a promising method to detect radio-opaque urinary tract stones with satisfactory sensitivity although the PPV could still be improved. The CAD model detects urinary tract stones quickly and automatically and has the potential to become a helpful screening modality especially for primary care physicians for diagnosing urolithiasis. Further study using a higher volume of data would improve the diagnostic performance of CAD models to detect urinary tract stones on a plain X-ray.

scholarly journals Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers

10.2196/18089 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e18089
Author(s):  
Ryoungwoo Jang ◽  
Namkug Kim ◽  
Miso Jang ◽  
Kyung Hwa Lee ◽  
Sang Min Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Computer Aided Diagnosis ◽  
X Ray ◽  
The Public ◽  
Computer Aided ◽  
Chest X Ray ◽  
Public Datasets ◽  
Aided Diagnosis

Background Computer-aided diagnosis on chest x-ray images using deep learning is a widely studied modality in medicine. Many studies are based on public datasets, such as the National Institutes of Health (NIH) dataset and the Stanford CheXpert dataset. However, these datasets are preprocessed by classical natural language processing, which may cause a certain extent of label errors. Objective This study aimed to investigate the robustness of deep convolutional neural networks (CNNs) for binary classification of posteroanterior chest x-ray through random incorrect labeling. Methods We trained and validated the CNN architecture with different noise levels of labels in 3 datasets, namely, Asan Medical Center-Seoul National University Bundang Hospital (AMC-SNUBH), NIH, and CheXpert, and tested the models with each test set. Diseases of each chest x-ray in our dataset were confirmed by a thoracic radiologist using computed tomography (CT). Receiver operating characteristic (ROC) and area under the curve (AUC) were evaluated in each test. Randomly chosen chest x-rays of public datasets were evaluated by 3 physicians and 1 thoracic radiologist. Results In comparison with the public datasets of NIH and CheXpert, where AUCs did not significantly drop to 16%, the AUC of the AMC-SNUBH dataset significantly decreased from 2% label noise. Evaluation of the public datasets by 3 physicians and 1 thoracic radiologist showed an accuracy of 65%-80%. Conclusions The deep learning–based computer-aided diagnosis model is sensitive to label noise, and computer-aided diagnosis with inaccurate labels is not credible. Furthermore, open datasets such as NIH and CheXpert need to be distilled before being used for deep learning–based computer-aided diagnosis.

scholarly journals Application of Multi Layer Neural Network in Medical Diagnosis: an Efficient Survey

2018 ◽  
Vol 7 (3.34) ◽  
pp. 493
Author(s):  
Arti Rana ◽  
Arvind Singh Rawat ◽  
Himanshu Bahuguna ◽  
Anchit Bijalwan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Learning ◽  
Medical Diagnosis ◽  
Analytical Procedure ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

A enormous extent of facts is presently accessible to medical proficient; diverge from information of medical signs to a assortment of varieties of biological facts as well as imaging machines’ results. All kind of facts gives statistics that which is assessed as well as allocated for exacting pathology throughout the investigative procedure. For restructuring the analytical procedure on a day-to-day basis practice and evade misdiagnosis, artificial intelligence techniques (artificial neural networks, computer aided diagnosis) are able to employ. These adaptive learning techniques could be hold different kinds of medicinal facts and amalgamate them into characterized outcomes.In this paper, we have concisely analyzed and deliberated about competencies, philosophy as well as restrictions of ANN in medicinal analysis concluded elected paradigms.  

scholarly journals Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images

2020 ◽  
Author(s):  
Mugahed A. Al-antari ◽  
Cam-Hao Hua ◽  
Sungyoung Lee ◽  
Jaehun Bang
Keyword(s):  
Deep Learning ◽  
Respiratory Disease ◽  
Respiratory Diseases ◽  
Computer Aided Diagnosis ◽  
World Health ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Chest X Ray ◽  
Aided Diagnosis

Abstract Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as a previously unknown respiratory disease in Wuhan, Hubei Province, China. The world health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 based on the full resolution of digital X-ray images is the key to efficiently assisting patients by enabling physicians to reach a fast and accurate diagnosis decision. In this paper, a simultaneous deep learning computer-aided diagnosis (CAD) system based on the YOLO predictor is proposed that can detect and diagnose COVID-19, differentiating it from eight other respiratory diseases: atelectasis, infiltration, pneumothorax, masses, effusion, pneumonia, cardiomegaly, and nodules. The proposed CAD system was assessed via five-fold tests for the multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system was trained with an annotated training set of 50,490 chest X-ray images. The regions on the entire X-ray images with lesions suspected of being due to COVID-19 were simultaneously detected and classified end-to-end via the proposed CAD predictor, achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. Most test images from patients with confirmed COVID-19 and other respiratory diseases were correctly predicted, achieving average intersection over union (IoU) greater than 90%. Applying deep learning regularizers of data balancing and augmentation improved the COVID-19 diagnostic performance by 6.64% and 12.17% in terms of the overall accuracy and the F1-score, respectively. It is feasible to achieve a diagnosis based on individual chest X-ray images with the proposed CAD system within 0.0093 s. Thus, the CAD system presented in this paper can make a prediction at the rate of 108 frames/s (FPS), which is close to real-time. The proposed deep learning CAD system can reliably differentiate COVID-19 from other respiratory diseases. The proposed deep learning model seems to be a reliable tool that can be used to practically assist health care systems, patients, and physicians.

scholarly journals Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 107
Author(s):  
Krit Sriporn ◽  
Cheng-Fa Tsai ◽  
Chia-En Tsai ◽  
Paohsi Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lung Disease ◽  
Activation Function ◽  
Computer Aided Diagnosis ◽  
Neural Network Models ◽  
Processing Technologies ◽  
Lung Lesions ◽  
Computer Aided ◽  
Aided Diagnosis

Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.

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