scholarly journals SBC-Based Cataract Detection System using Deep Convolutional Neural Network with Transfer Learning Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 4605-4613
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
System Development ◽  
Deep Convolutional Neural Network ◽  
Raspberry Pi ◽  
Significant Difference ◽  
Cataract Patients

This Raspberry Pi Single Board Computer-Based Cataract Detection System using Deep Convolutional Neural Network through GoogLeNet Transfer Learning and MATLAB digital image processing paradigm based on Lens Opacities Classification System III with Python application, which would capture the image of the eyes of cataract patients to detect the type of cataract without using dilating drops. Additionally, the system could also determine the severity, grade, color or area, and hardness of cataract. It would also display, save, search and print the partial diagnosis that can be done to the patients. Descriptive quantitative research, Waterfall System Development Life Cycle and Evolutionary Prototyping Models was used as the methodologies of this study. Cataract patients and ophthalmologists of one of the eye clinics in City of Biñan, Laguna, as well as engineers and information technology professionals tested the system and also served as respondents to the conducted survey. Obtained results indicated that the detection of cataract and its characteristics using the system were accurate and reliable, which has a significant difference from the current eye examination for cataract. Generally, this would be a modern cataract detection system for all Cataract patients

scholarly journals МОДЕЛЬ ТА АЛГОРИТМ НАВЧАННЯ СИСТЕМИ ДЕТЕКТУВАННЯ МАЛОРОЗМІРНИХ ОБ’ЄКТІВ ДЛЯ МАЛОГАБАРИТНИХ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ

2018 ◽  
pp. 41-52
Author(s):  
В’ячеслав Васильович Москаленко ◽  
Альона Сергіївна Москаленко ◽  
Артем Геннадійович Коробов ◽  
Микола Олександрович Зарецький ◽  
Віктор Анатолійович Семашко
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Small Object ◽  
Feature Maps ◽  
Feature Map ◽  
High Level

The efficient model and learning algorithm of the small object detection system for compact aerial vehicle under conditions of restricted computing resources and the limited volume of the labeled learning set are developed. The four-stage learning algorithm of the object detector is proposed. At the first stage, selecting the type of deep convolutional neural network and the number of low-level layers that is pretrained on the ImageNet dataset for reusing takes place. The second stage involves unsupervised learning of high-level convolutional sparse coding layers using the modification of growing neural gas to automatically determine the required number of neurons and provide optimal distributions of the neurons over the data. Its application makes it possible to utilize the unlabeled learning datasets for the adaptation of the high-level feature description to the domain application area. At the third stage, the output feature map is formed by concatenation of feature maps from the different level of the deep convolutional neural network. At that, there is a reduction of output feature map using principal component analysis and followed by the building of decision rules. In order to perform the classification analysis of output, feature map is proposed to use information-extreme classifier learning on principles of boosting. Besides that, the orthogonal incremental extreme learning machine is used to build the regression model for the predict bounding box of the detected small object. The last stage involves fine-tuning of high-level layers of deep network using simulated annealing metaheuristic algorithm in order to approximate the global optimum of the complex criterion of learning efficiency of detection model. As a result of the use of proposed approach has been achieved 96% correctly detection of objects on the images of the open test dataset which indicates the suitability of the model and learning algorithm for practical use. In this case, the size of the learning dataset that has been used to construct the model was 500 unlabeled and 200 labeled learning samples

scholarly journals Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 652 ◽  
Author(s):  
Carlo Augusto Mallio ◽  
Andrea Napolitano ◽  
Gennaro Castiello ◽  
Francesco Maria Giordano ◽  
Pasquale D'Alessio ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Learning Algorithm ◽  
Checkpoint Inhibitor ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite

2021 ◽  
Author(s):  
Tathagat Banerjee ◽  
Aditya Jain ◽  
Sibi Chakkaravarthy Sethuraman ◽  
Suresh Chandra Satapathy ◽  
S. Karthikeyan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Deep Convolutional Neural Network ◽  
Malarial Parasite

scholarly journals DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Shashank A ◽  
vinayakumar R ◽  
Soman KP
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Input Data ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Traffic Data ◽  
Data Set ◽  
Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

Sign in / Sign up

Export Citation Format

Share Document