scholarly journals Identification of the Facial Features of Patients With Cancer: A Deep Learning–Based Pilot Study

10.2196/17234 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e17234 ◽  
Author(s):  
Bin Liang ◽  
Na Yang ◽  
Guosheng He ◽  
Peng Huang ◽  
Yong Yang
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Age Distribution ◽  
Image Data ◽  
Face Image ◽  
Facial Features ◽  
Facial Skin ◽  
Data Set ◽  
Patients With Cancer ◽  
Face Region

Background Cancer has become the second leading cause of death globally. Most cancer cases are due to genetic mutations, which affect metabolism and result in facial changes. Objective In this study, we aimed to identify the facial features of patients with cancer using the deep learning technique. Methods Images of faces of patients with cancer were collected to build the cancer face image data set. A face image data set of people without cancer was built by randomly selecting images from the publicly available MegaAge data set according to the sex and age distribution of the cancer face image data set. Each face image was preprocessed to obtain an upright centered face chip, following which the background was filtered out to exclude the effects of nonrelative factors. A residual neural network was constructed to classify cancer and noncancer cases. Transfer learning, minibatches, few epochs, L2 regulation, and random dropout training strategies were used to prevent overfitting. Moreover, guided gradient-weighted class activation mapping was used to reveal the relevant features. Results A total of 8124 face images of patients with cancer (men: n=3851, 47.4%; women: n=4273, 52.6%) were collected from January 2018 to January 2019. The ages of the patients ranged from 1 year to 70 years (median age 52 years). The average faces of both male and female patients with cancer displayed more obvious facial adiposity than the average faces of people without cancer, which was supported by a landmark comparison. When testing the data set, the training process was terminated after 5 epochs. The area under the receiver operating characteristic curve was 0.94, and the accuracy rate was 0.82. The main relative feature of cancer cases was facial skin, while the relative features of noncancer cases were extracted from the complementary face region. Conclusions In this study, we built a face data set of patients with cancer and constructed a deep learning model to classify the faces of people with and those without cancer. We found that facial skin and adiposity were closely related to the presence of cancer.

scholarly journals Identification of the Facial Features of Patients With Cancer: A Deep Learning–Based Pilot Study (Preprint)

2019 ◽  
Author(s):  
Bin Liang ◽  
Na Yang ◽  
Guosheng He ◽  
Peng Huang ◽  
Yong Yang
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Age Distribution ◽  
Image Data ◽  
Face Image ◽  
Facial Features ◽  
Facial Skin ◽  
Data Set ◽  
Patients With Cancer ◽  
Face Region

BACKGROUND Cancer has become the second leading cause of death globally. Most cancer cases are due to genetic mutations, which affect metabolism and result in facial changes. OBJECTIVE In this study, we aimed to identify the facial features of patients with cancer using the deep learning technique. METHODS Images of faces of patients with cancer were collected to build the cancer face image data set. A face image data set of people without cancer was built by randomly selecting images from the publicly available MegaAge data set according to the sex and age distribution of the cancer face image data set. Each face image was preprocessed to obtain an upright centered face chip, following which the background was filtered out to exclude the effects of nonrelative factors. A residual neural network was constructed to classify cancer and noncancer cases. Transfer learning, minibatches, few epochs, L2 regulation, and random dropout training strategies were used to prevent overfitting. Moreover, guided gradient-weighted class activation mapping was used to reveal the relevant features. RESULTS A total of 8124 face images of patients with cancer (men: n=3851, 47.4%; women: n=4273, 52.6%) were collected from January 2018 to January 2019. The ages of the patients ranged from 1 year to 70 years (median age 52 years). The average faces of both male and female patients with cancer displayed more obvious facial adiposity than the average faces of people without cancer, which was supported by a landmark comparison. When testing the data set, the training process was terminated after 5 epochs. The area under the receiver operating characteristic curve was 0.94, and the accuracy rate was 0.82. The main relative feature of cancer cases was facial skin, while the relative features of noncancer cases were extracted from the complementary face region. CONCLUSIONS In this study, we built a face data set of patients with cancer and constructed a deep learning model to classify the faces of people with and those without cancer. We found that facial skin and adiposity were closely related to the presence of cancer.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

scholarly journals Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Jose M. Castillo T. ◽  
Muhammad Arif ◽  
Martijn P. A. Starmans ◽  
Wiro J. Niessen ◽  
Chris H. Bangma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Model Development ◽  
Learning Model ◽  
Multiparametric Mri ◽  
Data Sets ◽  
Data Set ◽  
Test Sets ◽  
Deep Learning Model

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

scholarly journals Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dongsheng Wang ◽  
Jun Feng ◽  
Xinpeng Zhao ◽  
Yeping Bai ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cement Mortar ◽  
Recognition Accuracy ◽  
Image Data ◽  
Concrete Lining ◽  
Learning Method ◽  
Recognition Method ◽  
Data Set ◽  
Recognition Model ◽  
High Recognition Accuracy

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

scholarly journals Using Deep Learning for Image-Based Potato Tuber Disease Detection

2019 ◽  
Vol 109 (6) ◽  
pp. 1083-1087 ◽  
Author(s):  
Dor Oppenheim ◽  
Guy Shani ◽  
Orly Erlich ◽  
Leah Tsror
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Image Data ◽  
Object Identification ◽  
Plant Diseases ◽  
Disease Classification ◽  
Successful Implementation ◽  
Potato Tubers ◽  
Data Set ◽  
Convolutional Networks

Many plant diseases have distinct visual symptoms, which can be used to identify and classify them correctly. This article presents a potato disease classification algorithm that leverages these distinct appearances and advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network, training it to classify the tubers into five classes: namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes, and diseases, was acquired, classified, and labeled manually by experts. The models were trained over different train-test splits to better understand the amount of image data needed to apply deep learning for such classification tasks. The models were tested over a data set of images taken using standard low-cost RGB (red, green, and blue) sensors and were tagged by experts, demonstrating high classification accuracy. This is the first article to report the successful implementation of deep convolutional networks, popular in object identification, to the task of disease identification in potato tubers, showing the potential of deep learning techniques in agricultural tasks.

Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms

Heart ◽  
2018 ◽  
Vol 104 (23) ◽  
pp. 1921-1928 ◽  
Author(s):  
Ming-Zher Poh ◽  
Yukkee Cheung Poh ◽  
Pak-Hei Chan ◽  
Chun-Ka Wong ◽  
Louise Pun ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Test Data ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Learning System ◽  
Training Data ◽  
Validation Data ◽  
Data Set

ObjectiveTo evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.MethodsWe trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists. Six established AF detectors based on handcrafted features were evaluated on the same test data set for performance comparison.ResultsIn the validation data set (3039 PPG waveforms) consisting of three sequential PPG waveforms from 1013 participants (mean (SD) age, 68.4 (12.2) years; 46.8% men), the prevalence of AF was 2.8%. The area under the receiver operating characteristic curve (AUC) of the DCNN for AF detection was 0.997 (95% CI 0.996 to 0.999) and was significantly higher than all the other AF detectors (AUC range: 0.924–0.985). The sensitivity of the DCNN was 95.2% (95% CI 88.3% to 98.7%), specificity was 99.0% (95% CI 98.6% to 99.3%), positive predictive value (PPV) was 72.7% (95% CI 65.1% to 79.3%) and negative predictive value (NPV) was 99.9% (95% CI 99.7% to 100%) using a single 17 s PPG waveform. Using the three sequential PPG waveforms in combination (<1 min in total), the sensitivity was 100.0% (95% CI 87.7% to 100%), specificity was 99.6% (95% CI 99.0% to 99.9%), PPV was 87.5% (95% CI 72.5% to 94.9%) and NPV was 100% (95% CI 99.4% to 100%).ConclusionsIn this evaluation of PPG waveforms from adults screened for AF in a real-world primary care setting, the DCNN had high sensitivity, specificity, PPV and NPV for detecting AF, outperforming other state-of-the-art methods based on handcrafted features.

A Deep Learning Method for Limited-View Intravascular Photoacoustic Image Reconstruction

2020 ◽  
Vol 10 (11) ◽  
pp. 2707-2713
Author(s):  
Zheng Sun ◽  
Xiangyang Yan
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Imaging Modality ◽  
Image Data ◽  
Structural Similarity ◽  
Simulated Image ◽  
Cross Sectional ◽  
Data Set ◽  
Reconstruction Methods ◽  
Under Sampling

Intravascular photoacoustic tomography (IVPAT) is a newly developed imaging modality in the interventional diagnosis and treatment of coronary artery diseases. Incomplete acoustic measurement caused by limitedview scanning of the detector in the vascular lumen results in under-sampling artifacts and distortion in the images reconstructed by using the standard reconstruction methods. A method for limited-view IVPAT image reconstruction based on deep learning is presented in this paper. A convolutional neural network (CNN) is constructed and trained with computer-simulated image data set. Then, the trained CNN is used to optimize the cross-sectional images of the vessel which are recovered from the incomplete photoacoustic measurements by using the standard time-reversal (TR) algorithm to obtain the images with the improved quality. Results of numerical demonstration indicate that the method can effectively reduce the image distortion and artifacts caused by the limited-view detection. Furthermore, it is superior to the compressed sensing (CS) method in recovering the unmeasured information of the imaging target with the structural similarity around 10% higher than CS reconstruction.

scholarly journals A Deep Recurrent Neural Network with Gated Momentum Unit for CT Image Reconstruction

2021 ◽  
Author(s):  
Masaki Ikuta
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Image Data ◽  
Observation Data ◽  
Ct Image ◽  
Image Study ◽  
Data Set ◽  
Image Domain ◽  
Ct Image Reconstruction

<div><div><div><p>Many algorithms and methods have been proposed for Computed Tomography (CT) image reconstruction, partic- ularly with the recent surge of interest in machine learning and deep learning methods. The majority of recently proposed methods are, however, limited to the image domain processing where deep learning is used to learn the mapping from a noisy image data set to a true image data set. While deep learning-based methods can produce higher quality images than conventional model-based post-processing algorithms, these methods have lim- itations. Deep learning-based methods used in the image domain are not sufficient for compensating for lost information during a forward and a backward projection in CT image reconstruction especially with a presence of high noise. In this paper, we propose a new Recurrent Neural Network (RNN) architecture for CT image reconstruction. We propose the Gated Momentum Unit (GMU) that has been extended from the Gated Recurrent Unit (GRU) but it is specifically designed for image processing inverse problems. This new RNN cell performs an iterative optimization with an accelerated convergence. The GMU has a few gates to regulate information flow where the gates decide to keep important long-term information and discard insignificant short- term detail. Besides, the GMU has a likelihood term and a prior term analogous to the Iterative Reconstruction (IR). This helps ensure estimated images are consistent with observation data while the prior term makes sure the likelihood term does not overfit each individual observation data. We conducted a synthetic image study along with a real CT image study to demonstrate this proposed method achieved the highest level of Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM). Also, we showed this algorithm converged faster than other well-known methods.</p></div></div></div>

scholarly journals Transfer learning with chest X-rays for ER patient classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jonathan Stubblefield ◽  
Mitchell Hervert ◽  
Jason L. Causey ◽  
Jake A. Qualls ◽  
Wei Dong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Current Model ◽  
Image Data ◽  
Learning Model ◽  
Image Features ◽  
Clinical Feature ◽  
Patient Classification ◽  
X Rays ◽  
Data Set ◽  
Deep Learning Model

AbstractOne of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.

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