Computer-Aided Diagnosis for Lung Lesion in Companion Animals from X-ray Images Using Deep Learning Techniques

2019 ◽  
Author(s):  
Pattaramanee Arsomngern ◽  
Nichakorn Numcharoenpinij ◽  
Jitpinun Piriyataravet ◽  
Wutiwong Teerapan ◽  
Woranich Hinthong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Companion Animals ◽  
Computer Aided Diagnosis ◽  
Lung Lesion ◽  
X Ray ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Trends in Computer-Aided Diagnosis Using Deep Learning Techniques: A Review of Recent Studies on Algorithm Development

2017 ◽  
Author(s):  
Bosede Iyiade Edwards ◽  
Nosiba Hisham Osman Khougali ◽  
Adrian David Cheok
Keyword(s):  
Deep Learning ◽  
Human Subjects ◽  
Binary Classification ◽  
Computer Aided Diagnosis ◽  
Network Architectures ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Aided ◽  
Multi Class Classification ◽  
Aided Diagnosis

With recent focus on deep neural network architectures for development of algorithms for computer-aided diagnosis (CAD), we provide a review of studies within the last 3 years (2015-2017) reported in selected top journals and conferences. 29 studies that met our inclusion criteria were reviewed to identify trends in this field and to inform future development. Studies have focused mostly on cancer-related diseases within internal medicine while diseases within gender-/age-focused fields like gynaecology/pediatrics have not received much focus. All reviewed studies employed image datasets, mostly sourced from publicly available databases (55.2%) and few based on data from human subjects (31%) and non-medical datasets (13.8%), while CNN architecture was employed in most (70%) of the studies. Confirmation of the effect of data manipulation on quality of output and adoption of multi-class rather than binary classification also require more focus. Future studies should leverage collaborations with medical experts to aid future with actual clinical testing with reporting based on some generally applicable index to enable comparison. Our next steps on plans for CAD development for osteoarthritis (OA), with plans to consider multi-class classification and comparison across deep learning approaches and unsupervised architectures were also highlighted.

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 A deep learning-based computer-aided diagnosis method of X-ray images for bone age assessment

2021 ◽  
Author(s):  
Shaowei Li ◽  
Bowen Liu ◽  
Shulian Li ◽  
Xinyu Zhu ◽  
Yang Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bone Age ◽  
Computer Aided Diagnosis ◽  
Experimental Comparison ◽  
Age Assessment ◽  
X Ray ◽  
Computer Aided ◽  
Bone Age Assessment ◽  
Diagnosis Method ◽  
Aided Diagnosis

AbstractBone age assessment using hand-wrist X-ray images is fundamental when diagnosing growth disorders of a child or providing a more patient-specific treatment. However, as clinical procedures are a subjective assessment, the accuracy depends highly on the doctor’s experience. Motivated by this, a deep learning-based computer-aided diagnosis method was proposed for performing bone age assessment. Inspired by clinical approaches and aimed to reduce expensive manual annotations, informative regions localization based on a complete unsupervised learning method was firstly performed and an image-processing pipeline was proposed. Subsequently, an image model with pre-trained weights as a backbone was utilized to enhance the reliability of prediction. The prediction head was implemented by a Multiple Layer Perceptron with one hidden layer. In compliance with clinical studies, gender information was an additional input to the prediction head by embedded into the feature vector calculated from the backbone model. After the experimental comparison study, the best results showed a mean absolute error of 6.2 months on the public RSNA dataset and 5.1 months on the additional dataset using MobileNetV3 as the backbone.

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.

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