A Method for Optimal Detection of Lung Cancer Based on Deep Learning Optimized by Marine Predators Algorithm

Lung cancer is the uncontrolled growth of cells in the lung that are made up of two spongy organs located in the chest. These cells may penetrate outside the lungs in a process called metastasis and spread to tissues and organs in the body. In this paper, using image processing, deep learning, and metaheuristic, an optimal methodology is proposed for early detection of this cancer. Here, we design a new convolutional neural network for this purpose. Marine predators algorithm is also used for optimal arrangement and better network accuracy. The method finally applied to RIDER dataset, and the results are compared with some pretrained deep networks, including CNN ResNet-18, GoogLeNet, AlexNet, and VGG-19. Final results showed higher results of the proposed method toward the compared techniques. The results showed that the proposed MPA-based method with 93.4% accuracy, 98.4% sensitivity, and 97.1% specificity provides the highest efficiency with the least error (1.6) toward the other state of the art methods.

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Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest

Applied Sciences ◽

10.3390/app10186425 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6425

Author(s):

Natascha Claudia D’Amico ◽

Rosa Sicilia ◽

Ermanno Cordelli ◽

Lorenzo Tronchin ◽

Carlo Greco ◽

...

Keyword(s):

Lung Cancer ◽

Overall Survival ◽

Deep Learning ◽

Small Cell Lung Cancer ◽

State Of The Art ◽

Local Binary Patterns ◽

Research Field ◽

Small Cell ◽

The Other ◽

Small Cell Lung

Lung cancer accounts for the largest amount of deaths worldwide with respect to the other oncological pathologies. To guarantee the most effective cure to patients for such aggressive tumours, radiomics is increasing as a novel and promising research field that aims at extracting knowledge from data in terms of quantitative measures that are computed from diagnostic images, with prognostic and predictive ends. This knowledge could be used to optimize current treatments and to maximize their efficacy. To this end, we hereby study the use of such quantitative biomarkers computed from CT images of patients affected by Non-Small Cell Lung Cancer to predict Overall Survival. The main contributions of this work are two: first, we consider different volumes of interest for the same patient to find out whether the volume surrounding the visible lesions can provide useful information; second, we introduce 3D Local Binary Patterns, which are texture measures scarcely explored in radiomics. As further validation, we show that the proposed signature outperforms not only the features automatically computed by a deep learning-based approach, but also another signature at the state-of-the-art using other handcrafted features.

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Bitstream-Based Neural Network for Scalable, Efficient, and Accurate Deep Learning Hardware

Frontiers in Neuroscience ◽

10.3389/fnins.2020.543472 ◽

2020 ◽

Vol 14 ◽

Author(s):

Hyeonuk Sim ◽

Jongeun Lee

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recognition Accuracy ◽

Fault Tolerant ◽

State Of The Art ◽

The Other ◽

Stochastic Computing ◽

Hardware Implementations ◽

Neuromorphic Hardware ◽

Very High

While convolutional neural networks (CNNs) continue to renew state-of-the-art performance across many fields of machine learning, their hardware implementations tend to be very costly and inflexible. Neuromorphic hardware, on the other hand, targets higher efficiency but their inference accuracy lags far behind that of CNNs. To bridge the gap between deep learning and neuromorphic computing, we present bitstream-based neural network, which is both efficient and accurate as well as being flexible in terms of arithmetic precision and hardware size. Our bitstream-based neural network (called SC-CNN) is built on top of CNN but inspired by stochastic computing (SC), which uses bitstreams to represent numbers. Being based on CNN, our SC-CNN can be trained with backpropagation, ensuring very high inference accuracy. At the same time our SC-CNN is deterministic, hence repeatable, and is highly accurate and scalable even to large networks. Our experimental results demonstrate that our SC-CNN is highly accurate up to ImageNet-targeting CNNs, and improves efficiency over conventional digital designs ranging through 50–100% in operations-per-area depending on the CNN and the application scenario, while losing <1% in recognition accuracy. In addition, our SC-CNN implementations can be much more fault-tolerant than conventional digital implementations.

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Fast Accurate and Automatic Brushstroke Extraction

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3429742 ◽

2021 ◽

Vol 17 (2) ◽

pp. 1-24

Author(s):

Yunfei Fu ◽

Hongchuan Yu ◽

Chih-Kuo Yeh ◽

Tong-Yee Lee ◽

Jian J. Zhang

Keyword(s):

Neural Network ◽

Efficient Algorithm ◽

Deep Neural Network ◽

High Efficiency ◽

State Of The Art ◽

High Reliability ◽

The Other ◽

Manual Annotation ◽

Stroke Extraction ◽

Art Research

Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.

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Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽

10.3390/s21093068 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3068

Author(s):

Soumaya Dghim ◽

Carlos M. Travieso-González ◽

Radim Burget

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Processing ◽

Deep Learning ◽

The Other ◽

Support Vector ◽

Learning Approaches ◽

Microscopic Images ◽

Trained Neural Network ◽

Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

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SHEDR: An End-to-End Deep Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media

INFORMS Journal on Computing ◽

10.1287/ijoc.2021.1112 ◽

2021 ◽

Author(s):

Yuheng Hu ◽

Yili Hong

Keyword(s):

Neural Network ◽

Social Media ◽

Deep Learning ◽

Event Detection ◽

Large Scale ◽

Short Term Memory ◽

State Of The Art ◽

Neural Network Models ◽

Neural Event ◽

End To End

Residents often rely on newspapers and television to gather hyperlocal news for community awareness and engagement. More recently, social media have emerged as an increasingly important source of hyperlocal news. Thus far, the literature on using social media to create desirable societal benefits, such as civic awareness and engagement, is still in its infancy. One key challenge in this research stream is to timely and accurately distill information from noisy social media data streams to community members. In this work, we develop SHEDR (social media–based hyperlocal event detection and recommendation), an end-to-end neural event detection and recommendation framework with a particular use case for Twitter to facilitate residents’ information seeking of hyperlocal events. The key model innovation in SHEDR lies in the design of the hyperlocal event detector and the event recommender. First, we harness the power of two popular deep neural network models, the convolutional neural network (CNN) and long short-term memory (LSTM), in a novel joint CNN-LSTM model to characterize spatiotemporal dependencies for capturing unusualness in a region of interest, which is classified as a hyperlocal event. Next, we develop a neural pairwise ranking algorithm for recommending detected hyperlocal events to residents based on their interests. To alleviate the sparsity issue and improve personalization, our algorithm incorporates several types of contextual information covering topic, social, and geographical proximities. We perform comprehensive evaluations based on two large-scale data sets comprising geotagged tweets covering Seattle and Chicago. We demonstrate the effectiveness of our framework in comparison with several state-of-the-art approaches. We show that our hyperlocal event detection and recommendation models consistently and significantly outperform other approaches in terms of precision, recall, and F-1 scores. Summary of Contribution: In this paper, we focus on a novel and important, yet largely underexplored application of computing—how to improve civic engagement in local neighborhoods via local news sharing and consumption based on social media feeds. To address this question, we propose two new computational and data-driven methods: (1) a deep learning–based hyperlocal event detection algorithm that scans spatially and temporally to detect hyperlocal events from geotagged Twitter feeds; and (2) A personalized deep learning–based hyperlocal event recommender system that systematically integrates several contextual cues such as topical, geographical, and social proximity to recommend the detected hyperlocal events to potential users. We conduct a series of experiments to examine our proposed models. The outcomes demonstrate that our algorithms are significantly better than the state-of-the-art models and can provide users with more relevant information about the local neighborhoods that they live in, which in turn may boost their community engagement.

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An Efficient Method for Detection of DDoS Attacks on the Web Using Deep Learning Algorithms

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/271042021 ◽

2021 ◽

Vol 10 (4) ◽

pp. 2821-2829

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

State Of The Art ◽

Ddos Attacks ◽

Problem Statement ◽

Neural Network Approach ◽

Learning Techniques ◽

Attack Data ◽

Deep Learning Neural Network

Recently, DDoS attacks is the most significant threat in network security. Both industry and academia are currently debating how to detect and protect against DDoS attacks. Many studies are provided to detect these types of attacks. Deep learning techniques are the most suitable and efficient algorithm for categorizing normal and attack data. Hence, a deep neural network approach is proposed in this study to mitigate DDoS attacks effectively. We used a deep learning neural network to identify and classify traffic as benign or one of four different DDoS attacks. We will concentrate on four different DDoS types: Slowloris, Slowhttptest, DDoS Hulk, and GoldenEye. The rest of the paper is organized as follow: Firstly, we introduce the work, Section 2 defines the related works, Section 3 presents the problem statement, Section 4 describes the proposed methodology, Section 5 illustrate the results of the proposed methodology and shows how the proposed methodology outperforms state-of-the-art work and finally Section VI concludes the paper.

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AI-MHC: an allele-integrated deep learning framework for improving Class I & Class II HLA-binding predictions

10.1101/318881 ◽

2018 ◽

Cited By ~ 5

Author(s):

John-William Sidhom ◽

Drew Pardoll ◽

Alexander Baras

Keyword(s):

Neural Network ◽

Deep Learning ◽

Immune System ◽

State Of The Art ◽

Class Ii ◽

Class I ◽

Binding Prediction ◽

Translational Invariance ◽

Mhc Molecules ◽

Improved Performance

AbstractMotivationThe immune system has potential to present a wide variety of peptides to itself as a means of surveillance for pathogenic invaders. This means of surveillances allows the immune system to detect peptides derives from bacterial, viral, and even oncologic sources. However, given the breadth of the epitope repertoire, in order to study immune responses to these epitopes, investigators have relied on in-silico prediction algorithms to help narrow down the list of candidate epitopes, and current methods still have much in the way of improvement.ResultsWe present Allele-Integrated MHC (AI-MHC), a deep learning architecture with improved performance over the current state-of-the-art algorithms in human Class I and Class II MHC binding prediction. Our architecture utilizes a convolutional neural network that improves prediction accuracy by 1) allowing one neural network to be trained on all peptides for all alleles of a given class of MHC molecules by making the allele an input to the net and 2) introducing a global max pooling operation with an optimized kernel size that allows the architecture to achieve translational invariance in MHC-peptide binding analysis, making it suitable for sequence analytics where a frame of interest needs to be learned in a longer, variable length sequence. We assess AI-MHC against internal independent test sets and compare against all algorithms in the IEDB automated server benchmarks, demonstrating our algorithm achieves state-of-the-art for both Class I and Class II prediction.Availability and ImplementationAI-MHC can be used via web interface at baras.pathology.jhu.edu/[email protected]

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Covid-19 detection via deep neural network and occlusion sensitivity maps

10.36227/techrxiv.14100890 ◽

2021 ◽

Author(s):

Noor Ahmad ◽

Muhammad Aminu ◽

Mohd Halim Mohd Noor

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

State Of The Art ◽

Color Images ◽

Fine Tuning ◽

Training Dataset ◽

Learning Approaches ◽

Learning Models ◽

Sensitivity Maps

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

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Forest Fire Detection via Feature Entropy Guided Neural Network

Entropy ◽

10.3390/e24010128 ◽

2022 ◽

Vol 24 (1) ◽

pp. 128

Author(s):

Zhenwei Guan ◽

Feng Min ◽

Wei He ◽

Wenhua Fang ◽

Tao Lu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Forest Fire ◽

State Of The Art ◽

Fire Detection ◽

High Entropy ◽

Training Samples ◽

Feature Information ◽

Forest Fire Detection ◽

Entropy Source

Forest fire detection from videos or images is vital to forest firefighting. Most deep learning based approaches rely on converging image loss, which ignores the content from different fire scenes. In fact, complex content of images always has higher entropy. From this perspective, we propose a novel feature entropy guided neural network for forest fire detection, which is used to balance the content complexity of different training samples. Specifically, a larger weight is given to the feature of the sample with a high entropy source when calculating the classification loss. In addition, we also propose a color attention neural network, which mainly consists of several repeated multiple-blocks of color-attention modules (MCM). Each MCM module can extract the color feature information of fire adequately. The experimental results show that the performance of our proposed method outperforms the state-of-the-art methods.

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.21203/rs.3.rs-91905/v1 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Prediction Accuracy ◽

Data Science ◽

State Of The Art ◽

Hybrid Models ◽

The Other ◽

Learning Models ◽

Comprehensive Review

Abstract This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

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