Application of Deep Neural Networks for Disease Diagnosis Through Medical Data Sets

Abstract Transfer learning from natural images is well used in deep neural networks (DNNs) for medical image classification to achieve computer-aided clinical diagnosis. Although the adversarial vulnerability of DNNs hinders practical applications owing to the high stakes of diagnosis, adversarial attacks are expected to be limited because training data — which are often required for adversarial attacks — are generally unavailable in terms of security and privacy preservation. Nevertheless, we hypothesized that adversarial attacks are also possible using natural images because pre-trained models do not change significantly after fine-tuning. We focused on three representative DNN-based medical image classification tasks (i.e., skin cancer, referable diabetic retinopathy, and pneumonia classifications) and investigated whether medical DNN models with transfer learning are vulnerable to universal adversarial perturbations (UAPs), generated using natural images. UAPs from natural images are useful for both non-targeted and targeted attacks. The performance of UAPs from natural images was significantly higher than that of random controls, although slightly lower than that of UAPs from training images. Vulnerability to UAPs from natural images was observed between different natural image datasets and between different model architectures. The use of transfer learning causes a security hole, which decreases the reliability and safety of computer-based disease diagnosis. Model training from random initialization (without transfer learning) reduced the performance of UAPs from natural images; however, it did not completely avoid vulnerability to UAPs. The vulnerability of UAPs from natural images will become a remarkable security threat.

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Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

Expert Systems ◽

10.1111/exsy.12879 ◽

2021 ◽

Author(s):

Jafar A. Alzubi ◽

Omar A. Alzubi ◽

Majdi Beseiso ◽

Anil Kumar Budati ◽

K. Shankar

Keyword(s):

Neural Networks ◽

Data Transmission ◽

Deep Neural Networks ◽

Homomorphic Encryption ◽

Medical Data

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Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2839092 ◽

2018 ◽

Vol 15 (9) ◽

pp. 1451-1455 ◽

Cited By ~ 23

Author(s):

Grant J. Scott ◽

Kyle C. Hagan ◽

Richard A. Marcum ◽

James Alex Hurt ◽

Derek T. Anderson ◽

...

Keyword(s):

Neural Networks ◽

High Resolution ◽

Deep Neural Networks ◽

Image Data ◽

Data Sets ◽

Resolution Image ◽

High Resolution Image

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Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation

Energies ◽

10.3390/en14196156 ◽

2021 ◽

Vol 14 (19) ◽

pp. 6156

Author(s):

Stefan Hensel ◽

Marin B. Marinov ◽

Michael Koch ◽

Dimitar Arnaudov

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Data Sets ◽

Cloud Detection ◽

Camera System ◽

Data Set ◽

Network Methods ◽

Segmentation Approach ◽

Coverage Prediction ◽

Short Time

This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.

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A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

Frontiers in Genetics ◽

10.3389/fgene.2021.798840 ◽

2021 ◽

Vol 12 ◽

Author(s):

Osval A. Montesinos-López ◽

Abelardo Montesinos-López ◽

Brandon A. Mosqueda-González ◽

Alison R. Bentley ◽

Morten Lillemo ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Plant Breeding ◽

Deep Neural Networks ◽

Reference Population ◽

Calibration Method ◽

Data Sets ◽

Simple Method ◽

Continuous Response ◽

Predicting Performance

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.

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Modified Deep Neural Networks for Dog Breeds Identification

10.20944/preprints201812.0232.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aydin Ayanzadeh ◽

Sahand Vahidnia

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

State Of The Art ◽

The State ◽

Fine Tuning ◽

Test Accuracy ◽

Data Sets ◽

Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

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Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2019.07.008 ◽

2019 ◽

Vol 98 ◽

pp. 59-76 ◽

Cited By ~ 4

Author(s):

Rudresh Deepak Shirwaikar ◽

Dinesh Acharya U ◽

Krishnamoorthi Makkithaya ◽

Surulivelrajan M ◽

Shikhar Srivastava ◽

...

Keyword(s):

Neural Networks ◽

Medical Data ◽

Data Sets ◽

Neonatal Apnea

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SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification

Remote Sensing ◽

10.3390/rs12203358 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3358

Author(s):

Vasileios Syrris ◽

Ondrej Pesek ◽

Pierre Soille

Keyword(s):

Neural Networks ◽

Image Classification ◽

Supervised Classification ◽

Deep Neural Networks ◽

Satellite Image ◽

Data Retrieval ◽

Remote Sensing Image ◽

Training Data ◽

Data Sets ◽

Remote Sensing Image Classification

Automatic supervised classification with complex modelling such as deep neural networks requires the availability of representative training data sets. While there exists a plethora of data sets that can be used for this purpose, they are usually very heterogeneous and not interoperable. In this context, the present work has a twofold objective: (i) to describe procedures of open-source training data management, integration, and data retrieval, and (ii) to demonstrate the practical use of varying source training data for remote sensing image classification. For the former, we propose SatImNet, a collection of open training data, structured and harmonized according to specific rules. For the latter, two modelling approaches based on convolutional neural networks have been designed and configured to deal with satellite image classification and segmentation.

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Artificial Intelligence Explained for Nonexperts

Seminars in Musculoskeletal Radiology ◽

10.1055/s-0039-3401041 ◽

2020 ◽

Vol 24 (01) ◽

pp. 003-011 ◽

Cited By ~ 1

Author(s):

Narges Razavian ◽

Florian Knoll ◽

Krzysztof J. Geras

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Clinical Practice ◽

Medical Imaging ◽

Deep Neural Networks ◽

Large Data ◽

Large Data Sets ◽

Natural Images ◽

Data Sets ◽

Current State

AbstractArtificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.

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Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection

mSphere ◽

10.1128/msphere.00836-20 ◽

2020 ◽

Vol 5 (5) ◽

Author(s):

Artur Yakimovich ◽

Moona Huttunen ◽

Jerzy Samolej ◽

Barbara Clough ◽

Nagisa Yoshida ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Network Evolution ◽

Great Promise ◽

Data Sets ◽

Imaging Data ◽

Data Set ◽

Novel Strategy

ABSTRACT The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed “mimicry embedding,” for rapid application of neural network architecture-based analysis of pathogen imaging data sets. Embedding of a novel host-pathogen data set, such that it mimics a verified data set, enables efficient deep learning using high expressive capacity architectures and seamless architecture switching. We applied this strategy across various microbiological phenotypes, from superresolved viruses to in vitro and in vivo parasitic infections. We demonstrate that mimicry embedding enables efficient and accurate analysis of two- and three-dimensional microscopy data sets. The results suggest that transfer learning from pretrained network data may be a powerful general strategy for analysis of heterogeneous pathogen fluorescence imaging data sets. IMPORTANCE In biology, the use of deep neural networks (DNNs) for analysis of pathogen infection is hampered by a lack of large verified data sets needed for rapid network evolution. Artificial neural networks detect handwritten digits with high precision thanks to large data sets, such as MNIST, that allow nearly unlimited training. Here, we developed a novel strategy we call mimicry embedding, which allows artificial intelligence (AI)-based analysis of variable pathogen-host data sets. We show that deep learning can be used to detect and classify single pathogens based on small differences.

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