Diabetic Retinopathy Severity Detection using Convolutional Neural Network

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.

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Convolutional Neural Network for Iris Recognition

10.21203/rs.3.rs-244624/v1 ◽

2021 ◽

Author(s):

Wael Alnahari

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Iris Recognition ◽

Recognition System ◽

High Accuracy ◽

Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

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Computer-Aided Diagnosis of Thyroid Nodule from Ultrasound Images Using Transfer Learning from Deep Convolutional Neural Network Models

2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) ◽

10.1109/accthpa49271.2020.9213210 ◽

2020 ◽

Author(s):

O A Ajilisa ◽

V P Jagathyraj ◽

M K Sabu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Thyroid Nodule ◽

Transfer Learning ◽

Network Models ◽

Computer Aided Diagnosis ◽

Ultrasound Images ◽

Neural Network Models ◽

Computer Aided ◽

Aided Diagnosis

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Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning

European Neurology ◽

10.1159/000512985 ◽

2020 ◽

Vol 83 (6) ◽

pp. 602-614

Author(s):

Hidir Selcuk Nogay ◽

Hojjat Adeli

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Epileptic Seizure ◽

Binary Classification ◽

Classification Model ◽

Automatic Identification ◽

Eeg Signal ◽

Data Set

Introduction: The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations. Methods: In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data. Results: The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification. Discussion/Conclusion: The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.

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DeepFrag: A Deep Convolutional Neural Network for Fragment-based Lead Optimization

Chemical Science ◽

10.1039/d1sc00163a ◽

2021 ◽

Author(s):

Harrison Green ◽

David Ryan Koes ◽

Jacob D Durrant

Keyword(s):

Neural Network ◽

Machine Learning ◽

Drug Discovery ◽

Virtual Screening ◽

Convolutional Neural Network ◽

Binding Affinity ◽

Lead Optimization ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

Computer Aided

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is...

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Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

Physics in Medicine and Biology ◽

10.1088/1361-6560/aa93d4 ◽

2017 ◽

Vol 62 (23) ◽

pp. 8894-8908 ◽

Cited By ~ 65

Author(s):

Ravi K Samala ◽

Heang-Ping Chan ◽

Lubomir M Hadjiiski ◽

Mark A Helvie ◽

Kenny H Cha ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Computer Aided Diagnosis ◽

Deep Convolutional Neural Network ◽

Computer Aided ◽

Task Transfer ◽

Network Application ◽

Aided Diagnosis

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Fish Species Detection Application (FiSDA) in Leyte Gulf Using Convolutional Neural Network

Proceedings of Engineering and Technology Innovation ◽

10.46604/peti.2021.7892 ◽

2021 ◽

Vol 19 ◽

pp. 16-27

Author(s):

Gil Gabornes Dialogo ◽

Larmie Santos Feliscuzo ◽

Elmer Asilo Maravillas

Keyword(s):

Neural Network ◽

Machine Learning ◽

Data Collection ◽

Convolutional Neural Network ◽

Fish Species ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Accuracy Rate ◽

Species Detection ◽

Feature Similarity

This study presents an application that employs a machine-learning algorithm to identify fish species found in Leyte Gulf. It aims to help students and marine scientists with their identification and data collection. The application supports 467 fish species in which 6,918 fish images are used for training, validating, and testing the generated model. The model is trained for a total of 4,000 epochs. Using convolutional neural network (CNN) algorithm, the best model during training is observed at epoch 3,661 with an accuracy rate of 96.49% and a loss value of 0.1359. It obtains 82.81% with a loss value of 1.868 during validation and 80.58% precision during testing. The result shows that the model performs well in predicting Malatindok and Sapsap species, after obtaining the highest precision of 100%. However, Hangit is sometimes misclassified by the model after attaining 55% accuracy rate from the testing results because of its feature similarity to other species.

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Comparison of Convolutional Neural Network Model in Classification of Diabetic Retinopathy

Jurnal Penelitian Pos dan informatika ◽

10.17933/jppi.2019.090205 ◽

2019 ◽

Vol 9 (2) ◽

pp. 141

Author(s):

Hartanto Ignatius ◽

Ricky Chandra ◽

Nicholas Bohdan ◽

Abdi Dharma

Keyword(s):

Neural Network ◽

Diabetes Mellitus ◽

Machine Learning ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Contrast Enhancement ◽

Neural Network Model ◽

Reasonable Accuracy ◽

Deep Architecture

Untreated diabetes mellitus will cause complications, and one of the diseases caused by it is Diabetic Retinopathy (DR). Machine learning is one of the methods that can be used to classify DR. Convolutional Neural Network (CNN) is a branch of machine learning that can classify images with reasonable accuracy. The Messidor dataset, which has 1,200 images, is often used as a dataset for the DR classification. Before training the model, we carried out several data preprocessing, such as labeling, resizing, cropping, separation of the green channel of images, contrast enhancement, and changing image extensions. In this paper, we proposed three methods of DR classification: Simple CNN, Le-Net, and DRnet model. The accuracy of testing of the several models of test data was 46.7%, 51.1%, and 58.3% Based on the research, we can see that DR classification must use a deep architecture so that the feature of the DR can be recognized. In this DR classification, DRnet achieved better accuracy with an average of 9.4% compared to Simple CNN and Le-Net model.

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Transfer Learning Based MA Detection (TL-MAD)

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c5449.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 3653-3656

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Automatic Detection ◽

Microvascular Complication ◽

Fundus Images ◽

Main Symptom ◽

Retinal Fundus Images ◽

Retinal Fundus

Diabetic Retinopathy (DR) is a microvascular complication of Diabetes that can lead to blindness if it is severe. Microaneurysm (MA) is the initial and main symptom of DR. In this paper, an automatic detection of DR from retinal fundus images of publicly available dataset has been proposed using transfer learning with pre-trained model VGG16 based on Convolutional Neural Network (CNN). Our method achieves improvement in accuracy for MA detection using retinal fundus images in prediction of Diabetic Retinopathy.

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