Novel Computer Aided Diagnostic System Using Synergic Deep Learning Technique for Early Detection of Pancreatic Cancer

Pancreatic cancer (PC) in the more extensive sense alludes to in excess of 277 distinct kinds of cancer sickness. Researchers have recognized distinctive phase of pancreatic cancers, showing that few quality transformations are engaged with cancer pathogenesis. These quality transformations lead to unusual cell multiplication. Therefore, in this study we propose a Computer Aided Diagnosis (CAD) system using Synergic Inception ResNet-V2, Deep convoluted neural network architecture for the identification of PC cases from publically Usable CT images that could extract PC graphical functionality to include clinical diagnosis before the pathogenic examination, saving valuable time for disease prevention. Simulation results using MATLAB is shown to illustrate that quite promising results have been obtained in terms of accuracy in detecting patients infected with BC. Accuracy of 99.23 per cent is reached using the proposed deep learning method, which is better than all other state-of-the-art approaches available in the literature. The calculation time was found to be less than the other current 22 second process. The proximity of the suggested approach to the True Positive values in the ROC curve suggests a result that is greater than the other methods. The comparative study with Inception ResNet-V2 is based on separate test and training data at a rate of 90 percent-10 percent, 80 percent-20 percent and 70 percent-30% respectively, which shows the robustness of the proposed research work. Experimental findings show the proposed reliability of the device relative to other detection approaches. The proposed CAD device is fully automated and has thus proved to be a promising additional diagnostic tool for frontline clinical physicians.

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Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

Applied Sciences ◽

10.3390/app11157148 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7148

Author(s):

Bedada Endale ◽

Abera Tullu ◽

Hayoung Shi ◽

Beom-Soo Kang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Network Architecture ◽

Input Data ◽

Deep Neural Network ◽

Data Augmentation ◽

Object Classification ◽

Training Data ◽

Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

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Deep learning of stochastic contagion dynamics on complex networks

10.21203/rs.3.rs-36564/v1 ◽

2020 ◽

Author(s):

Charles Murphy ◽

Edward Laurence ◽

Antoine Allard

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Time Series Data ◽

Training Data ◽

Series Data ◽

Neural Network Architecture ◽

Complementary Approach ◽

Quantitative Accuracy ◽

Simplifying Assumptions ◽

Effective Models

Abstract Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically and/or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where the effective local mechanisms governing a dynamic are learned automatically from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using stochastic contagion dynamics of increasing complexity on static and temporal networks. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.

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Stock Price Prediction Using Deep Learning Models

10.36227/techrxiv.16640197 ◽

2021 ◽

Author(s):

Jaydip Sen ◽

Sidra Mehtab ◽

Gourab Nath

Keyword(s):

Deep Learning ◽

Stock Prices ◽

Stock Price ◽

Regression Models ◽

Research Work ◽

The Other ◽

Learning Models ◽

Stock Price Prediction ◽

Price Prediction ◽

Other Hand

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modeled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using five deep learning-based regression models. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of December 29, 2014 to July 31, 2020. Based on the NIFTY data during December 29, 2014 to December 28, 2018, we build two regression models using convolutional neural networks (CNNs), and three regression models using long-and-short-term memory (LSTM) networks for predicting the open values of the NIFTY 50 index records for the period December 31, 2018 to July 31, 2020. We adopted a multi-step prediction technique with walk-forward validation. The parameters of the five deep learning models are optimized using the grid-search technique so that the validation losses of the models stabilize with an increasing number of epochs in the model training, and the training and validation accuracies converge. Extensive results are presented on various metrics for all the proposed regression models. The results indicate that while both CNN and LSTM-based regression models are very accurate in forecasting the NIFTY 50 open values, the CNN model that previous one week’s data as the input is the fastest in its execution. On the other hand, the encoder-decoder convolutional LSTM model uses the previous two weeks’ data as the input is found to be the most accurate in its forecasting results.

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Scale-Covariant and Scale-Invariant Gaussian Derivative Networks

Journal of Mathematical Imaging and Vision ◽

10.1007/s10851-021-01057-9 ◽

2021 ◽

Author(s):

Tony Lindeberg

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Large Scale ◽

Hybrid Approach ◽

Multiple Scale ◽

Scale Space ◽

Training Data ◽

Scale Invariant ◽

Gaussian Derivative ◽

Space Operations

AbstractThis paper presents a hybrid approach between scale-space theory and deep learning, where a deep learning architecture is constructed by coupling parameterized scale-space operations in cascade. By sharing the learnt parameters between multiple scale channels, and by using the transformation properties of the scale-space primitives under scaling transformations, the resulting network becomes provably scale covariant. By in addition performing max pooling over the multiple scale channels, or other permutation-invariant pooling over scales, a resulting network architecture for image classification also becomes provably scale invariant. We investigate the performance of such networks on the MNIST Large Scale dataset, which contains rescaled images from the original MNIST dataset over a factor of 4 concerning training data and over a factor of 16 concerning testing data. It is demonstrated that the resulting approach allows for scale generalization, enabling good performance for classifying patterns at scales not spanned by the training data.

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HistoFlow: Label-Efficient and Interactive Deep Learning Cell Analysis

10.1101/2020.07.15.204891 ◽

2020 ◽

Author(s):

Tim Henning ◽

Benjamin Bergner ◽

Christoph Lippert

Keyword(s):

Machine Learning ◽

Deep Learning ◽

User Interface ◽

Network Architecture ◽

Training Data ◽

Cell Segmentation ◽

Annotation Tool ◽

Cell Analysis ◽

Instance Segmentation

Instance segmentation is a common task in quantitative cell analysis. While there are many approaches doing this using machine learning, typically, the training process requires a large amount of manually annotated data. We present HistoFlow, a software for annotation-efficient training of deep learning models for cell segmentation and analysis with an interactive user interface.It provides an assisted annotation tool to quickly draw and correct cell boundaries and use biomarkers as weak annotations. It also enables the user to create artificial training data to lower the labeling effort. We employ a universal U-Net neural network architecture that allows accurate instance segmentation and the classification of phenotypes in only a single pass of the network. Transfer learning is available through the user interface to adapt trained models to new tissue types.We demonstrate HistoFlow for fluorescence breast cancer images. The models trained using only artificial data perform comparably to those trained with time-consuming manual annotations. They outperform traditional cell segmentation algorithms and match state-of-the-art machine learning approaches. A user test shows that cells can be annotated six times faster than without the assistance of our annotation tool. Extending a segmentation model for classification of epithelial cells can be done using only 50 to 1500 annotations.Our results show that, unlike previous assumptions, it is possible to interactively train a deep learning model in a matter of minutes without many manual annotations.

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Deep learning of contagion dynamics on complex networks

Nature Communications ◽

10.1038/s41467-021-24732-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Charles Murphy ◽

Edward Laurence ◽

Antoine Allard

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Time Series Data ◽

Real Data ◽

Training Data ◽

Series Data ◽

Complementary Approach ◽

Quantitative Accuracy ◽

Simplifying Assumptions ◽

Effective Models

AbstractForecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.

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Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting

Energies ◽

10.3390/en14217378 ◽

2021 ◽

Vol 14 (21) ◽

pp. 7378

Author(s):

Pedro M. R. Bento ◽

Jose A. N. Pombo ◽

Maria R. A. Calado ◽

Silvio J. P. S. Mariano

Keyword(s):

Deep Learning ◽

New England ◽

Network Architecture ◽

Prediction Models ◽

Load Forecasting ◽

Training Data ◽

Constant Field ◽

Short Term ◽

Wide Range ◽

Short Term Load Forecasting

Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods.

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Mo1296 RELIMINARY RESULT OF COMPUTER AIDED DIAGNOSIS (CAD) PERFORMANCE USING DEEP LEARNING IN EUS-FNA CYTOLOGY OF PANCREATIC CANCER

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2018.04.1946 ◽

2018 ◽

Vol 87 (6) ◽

pp. AB434 ◽

Cited By ~ 2

Author(s):

Yusuke Hashimoto ◽

Izumi Ohno ◽

Hiroshi Imaoka ◽

Hideaki Takahashi ◽

Shuichi Mitsunaga ◽

...

Keyword(s):

Pancreatic Cancer ◽

Deep Learning ◽

Computer Aided Diagnosis ◽

Computer Aided ◽

Aided Diagnosis

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COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings

International Journal of Biomedical Imaging ◽

10.1155/2020/8828855 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Mohd Zulfaezal Che Azemin ◽

Radhiana Hassan ◽

Mohd Izzuddin Mohd Tamrin ◽

Mohd Adli Md Ali

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Clinical Findings ◽

Training Data ◽

Receiver Operating Curve ◽

Data Sets ◽

Data Set ◽

X Ray ◽

Chest X Ray ◽

Sensitivity Specificity

The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.

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Deep Learning Classification on Optical Coherence Tomography Retina Images

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420520023 ◽

2019 ◽

Vol 34 (08) ◽

pp. 2052002

Author(s):

Frank Y. Shih ◽

Himanshu Patel

Keyword(s):

Neural Networks ◽

Optical Coherence Tomography ◽

Deep Learning ◽

Network Architecture ◽

Data Augmentation ◽

Training Data ◽

Retinal Images ◽

Optical Coherence ◽

Rgb Images ◽

Testing Accuracy

This paper presents a novel deep learning classification technique applied on optical coherence tomography (OCT) retinal images. We propose the deep neural networks based on Vgg16 pre-trained network model. The OCT retinal image dataset consists of four classes, including three most common retina diseases and one normal retina scan. Because the scale of training data is not sufficiently large, we use the transfer learning technique. Since the convolutional neural networks are sensitive to a little data change, we use data augmentation to analyze the classified results on retinal images. The input grayscale OCT scan images are converted to RGB images using colormaps. We have evaluated different types of classifiers with variant parameters in training the network architecture. Experimental results show that testing accuracy of 99.48% can be obtained as combined on all the classes.

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