scholarly journals Using a Deep Learning System That Classifies Hypertensive Retinopathy Based on the Fundus Images of Patients of Wide Age

2021 ◽  
Vol 38 (1) ◽  
pp. 207-213
Author(s):  
Süleyman Burçin Şüyun ◽  
Şakir Taşdemir ◽  
Serkan Biliş ◽  
Alexandru Milea
Keyword(s):  
Deep Learning ◽  
Learning System ◽  
Hypertensive Retinopathy ◽  
Data Sets ◽  
Fundus Images ◽  
Local Data ◽  
Continuous Neural Networks ◽  
Deep Learning Model

Range throughout Turkey in this paper, the author trained the continuous neural networks, and used a total of 4,000 fundus images, including images with different degrees of fundus disorders and images without disorders, so that CNN can detect whether the patient has hypertension and arteriosclerosis according to macular degeneration in the fundus images. In order to obtain more effective results from the deep learning structure using convolutional neural network, this paper prepared more data sets on the basis of Turkey, combined with the local data sets to educate the deep learning model, so as to integrate the data globally, which can help standardize the results and improve the accuracy. The system is used to diagnose retinal vascular degeneration, such as fundus vascular disease and macular edema disease. Based on this basic understanding, the research has been used for the detection and classification of hypertensive retinopathy that has similar causes. The author also points out the limitations of the system. Among them, the most important limitation is the need for long-term financial sustainability.

scholarly journals A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection

2021 ◽  
Author(s):  
melissa de la pava ◽  
Hernan Rios ◽  
Francisco J. Rodríguez ◽  
Oscar J. Perdomo ◽  
Fabio A. González
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Model ◽  
Lesion Detection ◽  
Fundus Images ◽  
Retinal Lesion ◽  
Deep Learning Model

scholarly journals Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

2019 ◽  
Vol 9 (22) ◽  
pp. 4871 ◽  
Author(s):  
Quan Liu ◽  
Chen Feng ◽  
Zida Song ◽  
Joseph Louis ◽  
Jian Zhou
Keyword(s):  
Deep Learning ◽  
Model Comparison ◽  
Surveillance Systems ◽  
Comparison Study ◽  
Learning Models ◽  
The Core ◽  
Dump Trucks ◽  
Deep Learning Model ◽  
Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

scholarly journals Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chiaki Kuwada ◽  
Yoshiko Ariji ◽  
Yoshitaka Kise ◽  
Takuma Funakoshi ◽  
Motoki Fukuda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cleft Palate ◽  
Panoramic Radiography ◽  
False Positive Rate ◽  
Normal Subjects ◽  
Learning System ◽  
Panoramic Radiographs ◽  
Positive Rate ◽  
Aided Diagnosis

AbstractAlthough panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.

Detection and classification of arrhythmia using an explainable deep learning model

2021 ◽  
Author(s):  
Yong-Yeon Jo ◽  
Joon-myoung Kwon ◽  
Ki-Hyun Jeon ◽  
Yong-Hyeon Cho ◽  
Jae-Hyun Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Deep Learning Model

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.

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