A Machine Learning Approach to Detect Diabetic Retinopathy Using Convolutional Neural Network

2019 ◽  
pp. 255-264
Author(s):  
Muhammad Mahir Hasan Chowdhury ◽  
Nishat Tasnim Ahmed Meem ◽  
Marium-E-Jannat
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

2020 ◽  
Vol 6 ◽  
pp. e268 ◽  
Author(s):  
Abder-Rahman Ali ◽  
Jingpeng Li ◽  
Guang Yang ◽  
Sally Jane O’Shea
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Skin Lesion ◽  
Early Diagnosis ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Clinical Feature ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Sensitivity Specificity

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively.

scholarly journals Predicting blastocyst formation of day 3 embryos using a convolutional neural network (CNN): a machine learning approach

2019 ◽  
Vol 112 (3) ◽  
pp. e272-e273 ◽  
Author(s):  
Pietro Bortoletto ◽  
Manoj Kumar Kanakasabapathy ◽  
Prudhvi Thirumalaraju ◽  
Raghav Gupta ◽  
Rohan Pooniwala ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Approach ◽  
Blastocyst Formation ◽  
Machine Learning Approach ◽  
Day 3 Embryos

Source deghosting of coarsely-sampled common-receiver data using a convolutional neural network

Geophysics ◽  
2021 ◽  
pp. 1-48
Author(s):  
Jan-Willem Vrolijk ◽  
Gerrit Blacquiere
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Field Data ◽  
Simulated Data ◽  
Training Data ◽  
Learning Approach ◽  
Receiver Domain ◽  
Machine Learning Approach ◽  
The Common

It is well known that source deghosting can best be applied to common-receiver gathers, while receiver deghosting can best be applied to common-shot records. The source-ghost wavefield observed in the common-shot domain contains the imprint of the subsurface, which complicates source deghosting in common-shot domain, in particular when the subsurface is complex. Unfortunately, the alternative, i.e., the common-receiver domain, is often coarsely sampled, which complicates source deghosting in this domain as well. To solve the latter issue, we propose to train a convolutional neural network to apply source deghosting in this domain. We subsample all shot records with and without the receiver ghost wavefield to obtain the training data. Due to reciprocity this training data is a representative data set for source deghosting in the coarse common-receiver domain. We validate the machine-learning approach on simulated data and on field data. The machine learning approach gives a significant uplift to the simulated data compared to conventional source deghosting. The field-data results confirm that the proposed machine-learning approach is able to remove the source-ghost wavefield from the coarsely-sampled common-receiver gathers.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

scholarly journals Diabetic Retinopathy Severity Detection using Convolutional Neural Network

2020 ◽  
Vol 32 ◽  
pp. 01012
Author(s):  
Mayank Shete ◽  
Saahil Sabnis ◽  
Srijan Rai ◽  
Gajanan Birajdar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Feature Learning ◽  
Eye Diseases ◽  
Accuracy Rate ◽  
Irreversible Damage ◽  
Computer Aided

Diabetic Retinopathy is one of the most prominent eye diseases and is the leading cause of blindness amongst adults. Automatic detection of Diabetic Retinopathy is important to prevent irreversible damage to the eye-sight. Existing feature learning methods have a lesser accuracy rate in computer aided diagnostics; this paper proposes a method to further increase the accuracy. Machine learning can be used effectively for the diagnosis of this disease. CNN and transfer learning are used for the severity classification and have achieved an accuracy of 73.9 percent. The use of XGBoost classifier yielded an accuracy of 76.5 percent.

scholarly journals Generating Interactive Worlds with Text

2020 ◽  
Vol 34 (02) ◽  
pp. 1693-1700 ◽  
Author(s):  
Angela Fan ◽  
Jack Urbanek ◽  
Pratik Ringshia ◽  
Emily Dinan ◽  
Emma Qian ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Common Sense ◽  
Learning Approach ◽  
Adventure Game ◽  
Machine Learning Approach ◽  
Game Environment ◽  
Game Elements ◽  
Construction Algorithms ◽  
Game Environments

Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common-sense has to be encoded into arrangement of the elements. In this work, we investigate a machine learning approach for world creation using content from the multi-player text adventure game environment LIGHT (Urbanek et al. 2019). We introduce neural network based models to compositionally arrange locations, characters, and objects into a coherent whole. In addition to creating worlds based on existing elements, our models can generate new game content. Humans can also leverage our models to interactively aid in worldbuilding. We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.

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