Scalable Healthcare Assessment for Diabetic Patients Using Deep Learning on Multiple GPUs

2019 ◽  
Vol 15 (10) ◽  
pp. 5682-5689 ◽  
Author(s):  
Daniel Sierra-Sosa ◽  
Begonya Garcia-Zapirain ◽  
Cristian Castillo ◽  
Ibon Oleagordia ◽  
Roberto Nuno-Solinis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diabetic Patients ◽  
Multiple Gpus ◽  
Healthcare Assessment

scholarly journals Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Mirshahi ◽  
Pasha Anvari ◽  
Hamid Riazi-Esfahani ◽  
Mahsa Sardarinia ◽  
Masood Naseripour ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

Fast Training of Deep Learning Models over Multiple GPUs

2020 ◽  
Author(s):  
Xiaodong Yi ◽  
Ziyue Luo ◽  
Chen Meng ◽  
Mengdi Wang ◽  
Guoping Long ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Multiple Gpus ◽  
Fast Training

scholarly journals Early Prediction Model for Type-2 Diabetes Based on Lifestyle

2020 ◽  
Vol 32 ◽  
pp. 03053
Author(s):  
Kirti Hirnak ◽  
Nikita Chaudhari ◽  
Akshay Singh ◽  
Deepali Patil
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Deep Learning ◽  
Chronic Disease ◽  
Diabetic Patients ◽  
Early Prediction ◽  
Learning Approaches ◽  
Random Forest Algorithm ◽  
Blurry Vision

Nowadays diabetes has become a chronic disease that may cause many complications. There are some symptoms of diabetes such as increased appetite, blurry vision, and extreme fatigue, etc. As the increasing deformity in present years the number of diabetic patients from the whole world will reach to 642 million. Diabetes accuracy is very difficult to know so in order to cure this disease. These causes us to concentrate more there to make some changes that will reduce these numbers. So to minimize these numbers of diabetes, we researched various algorithms and methods. The proposed method focuses on extracting the attributes that gives a result in early detection of Diabetes Mellitus in patients. Various existing processes provide just a result as the patient has diabetes or not which will require the patients to visit a diagnostic centers or to a doctor. So we proposed a system based on deep learning approaches that will help to solve a serious problem. These systems take collaborative inputs from dataset to give prediction with random forest algorithm which gives more accurate results.

scholarly journals Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020 ◽  
Vol 10 (6) ◽  
pp. 2021 ◽  
Author(s):  
Ibrahem Kandel ◽  
Mauro Castelli
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Main Idea ◽  
Diabetic Patients ◽  
Domain Transfer ◽  
Domain Transfer Learning ◽  
Enormous Number ◽  
Deep Learning Model

Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

scholarly journals A Novel System for the Identification of Diabetic Retinopathy using Computer Based Statistical Classification

2021 ◽  
Vol 12 (3) ◽  
pp. 4044-4059
Author(s):  
Sunil S S Et.al
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Early Stage ◽  
Diabetic Patients ◽  
Statistical Classification ◽  
Computer Based ◽  
Detection And Diagnosis ◽  
Different Characteristics ◽  
Early Detection And Diagnosis

Diabetes Retinopathy (DR) is an eye disorder that affects the human retina due to increased insulin levels in the blood. Early detection and diagnosis of DR is essential in the optimal treatment of diabetic patients. The current research is to develop controls for identifying different characteristics and differences in colour  retina and using different classifications. This therapeutic approach describes diabetes recovery from data collected from multiple fields including DRIDB0, DRIDB1, MESSIDOR, STARE and HRF. Here  machine learning, neural networks and deep learning algorithms issues are addressed with related topics such as Sensitivity, Precision, Accuracy, Error,   Specificity and F1-score, Mathews Correlation Coefficient (MCC) and coefficient of kappa are compared. Finally due to the deep learning strategy the results were more effective compared to other methods. The system can help ophthalmologists, to identify the symptoms of diabetes at an early stage, for better treatment and to improve the quality of life biology.

scholarly journals DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261698
Author(s):  
Mohsin Raza ◽  
Khuram Naveed ◽  
Awais Akram ◽  
Nema Salem ◽  
Amir Afaq ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Vessels ◽  
Medical Image Analysis ◽  
Retinal Disease ◽  
Disease Diagnosis ◽  
Vessel Segmentation ◽  
Diabetic Patients ◽  
Retinal Vasculature ◽  
Retinal Blood Vessels ◽  
Dense Aggregation

In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.

scholarly journals Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data

2021 ◽  
Vol 251 ◽  
pp. 03057
Author(s):  
Michael Andrews ◽  
Bjorn Burkle ◽  
Shravan Chaudhari ◽  
Davide Di Croce ◽  
Sergei Gleyzer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Top Quark ◽  
Learning Algorithms ◽  
Open Data ◽  
High Energy ◽  
Machine Learning Algorithms ◽  
Multiple Gpus ◽  
Event Identification ◽  
End To End

Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports on the implementation of the end-to-end deep learning with the CMS software framework and the scaling of the end-to-end deep learning with multiple GPUs. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation for particle and event identification. We demonstrate the end-to-end implementation on a top quark benchmark and perform studies with various hardware architectures including single and multiple GPUs and Google TPU.

scholarly journals An FPGA-Based Optimizer Design for Distributed Deep Learning with Multiple GPUs

2021 ◽  
Vol E104.D (12) ◽  
pp. 2057-2067
Author(s):  
Tomoya ITSUBO ◽  
Michihiro KOIBUCHI ◽  
Hideharu AMANO ◽  
Hiroki MATSUTANI
Keyword(s):  
Deep Learning ◽  
Multiple Gpus

scholarly journals An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Abdüssamed Erciyas ◽  
Necaattin Barışçı
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Vision Loss ◽  
Diabetic Patients ◽  
Disease Detection ◽  
Early Vision ◽  
Second Stage ◽  
22Nd Century ◽  
Harmful Effects

Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur. It is estimated that one third of more than half a million diabetic patients will have diabetic retinopathy by the 22nd century. Many effective methods have been proposed for disease detection with deep learning. In this study, unlike other studies, a deep learning-based method has been proposed in which diabetic retinopathy lesions are detected automatically and independently of datasets, and the detected lesions are classified. In the first stage of the proposed method, a data pool is created by collecting diabetic retinopathy data from different datasets. With Faster RCNN, lesions are detected, and the region of interests are marked. The images obtained in the second stage are classified using the transfer learning and attention mechanism. The method tested in Kaggle and MESSIDOR datasets reached 99.1% and 100% ACC and 99.9% and 100% AUC, respectively. When the obtained results are compared with other results in the literature, it is seen that more successful results are obtained.

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