scholarly journals Automatic Prediction of Meningioma Grade Image Based on Data Amplification and Improved Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hong Zhu ◽  
Qianhao Fang ◽  
Hanzhi He ◽  
Junfeng Hu ◽  
Daihong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Accuracy ◽  
World Health ◽  
Tumor Type ◽  
Grade Prediction ◽  
Health Organization ◽  
Deep Learning Model ◽  
The Brain

Meningioma is the second most commonly encountered tumor type in the brain. There are three grades of meningioma by the standards of the World Health Organization. Preoperative grade prediction of meningioma is extraordinarily important for clinical treatment planning and prognosis evaluation. In this paper, we present a new deep learning model for assisting automatic prediction of meningioma grades to reduce the recurrence of meningioma. Our model is based on an improved LeNet-5 model of convolutional neural network (CNN) and does not require the extraction of the diseased tissue, which can greatly enhance the efficiency. To address the issue of insufficient and unbalanced clinical data of meningioma images, we use an oversampling technique which allows us to considerably improve the accuracy of classification. Experiments on large clinical datasets show that our model can achieve quite high accuracy (i.e., as high as 83.33%) for the classification of meningioma images.

Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

2019 ◽  
Vol 10 (3) ◽  
pp. 60-73 ◽  
Author(s):  
Ravinder Ahuja ◽  
Daksh Jain ◽  
Deepanshu Sachdeva ◽  
Archit Garg ◽  
Chirag Rajput
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
American Sign Language ◽  
Sign Language ◽  
Automated System ◽  
World Health ◽  
American Sign ◽  
Hand Gestures ◽  
The World ◽  
Health Organization

Communicating through hand gestures with each other is simply called the language of signs. It is an acceptable language for communication among deaf and dumb people in this society. The society of the deaf and dumb admits a lot of obstacles in day to day life in communicating with their acquaintances. The most recent study done by the World Health Organization reports that very large section (around 360 million folks) present in the world have hearing loss, i.e. 5.3% of the earth's total population. This gives us a need for the invention of an automated system which converts hand gestures into meaningful words and sentences. The Convolutional Neural Network (CNN) is used on 24 hand signals of American Sign Language in order to enhance the ease of communication. OpenCV was used in order to follow up on further execution techniques like image preprocessing. The results demonstrated that CNN has an accuracy of 99.7% utilizing the database found on kaggle.com.

scholarly journals Deep Learning Based Tumor Type Classification Using Gene Expression Data

2018 ◽  
Author(s):  
Boyu Lyu ◽  
Anamul Haque
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tumor Type ◽  
Differential Analysis ◽  
Rna Seq ◽  
Heat Map ◽  
Standard Practices ◽  
Tumor Types ◽  
Pan Cancer

ABSTRACTDifferential analysis occupies the most significant portion of the standard practices of RNA-Seq analysis. However, the conventional method is matching the tumor samples to the normal samples, which are both from the same tumor type. The output using such method would fail in differentiating tumor types because it lacks the knowledge from other tumor types. Pan-Cancer Atlas provides us with abundant information on 33 prevalent tumor types which could be used as prior knowledge to generate tumor-specific biomarkers. In this paper, we embedded the high dimensional RNA-Seq data into 2-D images and used a convolutional neural network to make classification of the 33 tumor types. The final accuracy we got was 95.59%, higher than another paper applying GA/KNN method on the same dataset. Based on the idea of Guided Grad Cam, as to each class, we generated significance heat-map for all the genes. By doing functional analysis on the genes with high intensities in the heat-maps, we validated that these top genes are related to tumor-specific pathways, and some of them have already been used as biomarkers, which proved the effectiveness of our method. As far as we know, we are the first to apply convolutional neural network on Pan-Cancer Atlas for classification, and we are also the first to match the significance of classification with the importance of genes. Our experiment results show that our method has a good performance and could also apply in other genomics data.

scholarly journals Classification of Brain Tumor using Convolutional Neural Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 2841-2845
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Medical Science ◽  
Activation Functions ◽  
Case Scenario ◽  
Aggressive Disease ◽  
Fully Connected ◽  
The Brain

In medical science, brain tumor is the most common and aggressive disease and is known to be risk factors that have been confirmed by research. A brain tumor is the anomalous development of cell inside the brain. One conventional strategy to separate brain tumors is by reviewing the MRI pictures of the patient's mind. In this paper, we have designed a Convolutional Neural Network (CNN) to perceive whether the image contains tumor or not. We have designed 5 different CNN and examined each design on the basis of convolution layers, max-pooling, and flattening layers and activation functions. In each design we have made some changes on layers i.e. using different pooling layers in design 2 and 4, using different activation functions in design 2 and 3, and adding more Fully Connected layers in design 5. We examine their results and compare it with other designs. After comparing their results we find a best design out of 5 based on their accuracy. Utilizing our Convolutional neural network, we could accomplish a training accuracy and validation accuracy of design 3 at 100 epochs is 99.99% and 92.34%, best case scenario.

Deteksi Masker Pencegahan Covid19 Menggunakan Convolutional Neural Network Berbasis Android

2021 ◽  
Vol 5 (3) ◽  
pp. 576-583
Author(s):  
Purnama Nyoman ◽  
Putu Kusuma Negara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gradient Descent ◽  
Detection Method ◽  
World Health ◽  
The Public ◽  
Network Method ◽  
Public Areas ◽  
The Government ◽  
Health Organization

Masks are an important part of preventing Covid19 disease.The World Health Organization (WHO) have also recommended  the community use masks when doing activities in public areas. There are many types of masks that are used to cover the nose and mouth.  In general, there are about 3 types of masks that are commonly used by the public today, namely medical masks, N95 and cloth masks. This study aims to detect the type of mask used by the community. So that it can make easier for the government to apply discipline in COVID-19 health protocol. The detection method used in this study is a convolutional neural network (CNN). The first step is acquisition of knowledge, which first collects the types of masks on the market, followed by the representation of that knowledge before being modeled into a mathematical calculation formula, which will then be processed using the Convolutional Neural Network method. The system will be carried out by analyzing the recall value, its precision and accuracy.Testing process is carried out on an Android-based device  and the mobilenetV2 framework. In this study, the accuracy value is 90% using ADAM Optimization and 80 % using Gradient descent optimization.

scholarly journals Klasifikasi Data Berat Bayi Lahir Menggunakan Weighted Probabilistic Neural Network (WPNN) (Studi Kasus di Rumah Sakit Islam Sultan Agung Semarang)

2017 ◽  
Vol 10 (1) ◽  
pp. 61
Author(s):  
Hasbi Yasin ◽  
Dwi Ispriyansti
Keyword(s):  
Neural Network ◽  
Low Birthweight ◽  
Probabilistic Neural Network ◽  
High Accuracy ◽  
Training Data ◽  
World Health ◽  
Model Accuracy ◽  
Health Organization ◽  
Very High ◽  
Length Of Gestation

Low Birthweight (LBW) is one of the causes of infant mortality. Birthweight is the weight of babies who weighed within one hour after birth. Low birthweight has been defined by the World Health Organization (WHO) as weight at birth of less than 2,500 grams (5.5 pounds). There are several factors that influence the BWI such as maternal age, length of gestation, body weight, height, blood pressure, hemoglobin and parity. This study uses a Weighted Probabilistic Neural Network (WPNN) to classify the birthweight in RSI Sultan Agung Semarang based on these factors. The results showed that the birthweight classification using WPNN models have a very high accuracy. This is shown by the model accuracy of 98.75% using the training data and 94.44% using the testing data.Keywords:Birthweight, Classification, LBW, WPNN.

scholarly journals Classification of brain lesions on magnetic resonance imaging using superpixel, PSO and convolutional neural network

2019 ◽  
Author(s):  
Carolina L. S. Cipriano ◽  
Giovanni L. F. Da Silva ◽  
Jonnison L. Ferreira ◽  
Aristófanes C. Silva ◽  
Anselmo Cardoso De Paiva
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Active Regions ◽  
Pso Algorithm ◽  
Resonance Imaging ◽  
Mr Images ◽  
Clinical Routine ◽  
The Brain ◽  
Manual Classification

One of the most severe and common brain tumors is gliomas. Manual classification of injuries of this type is a laborious task in the clinical routine. Therefore, this work proposes an automatic method to classify lesions in the brain in 3D MR images based on superpixels, PSO algorithm and convolutional neural network. The proposed method obtained results for the complete, central and active regions, an accuracy of 87.88%, 70.51%, 80.08% and precision of 76%, 84%, 75% for the respective regions. The results demonstrate the difficulty of the network in the classification of the regions found in the lesions.

scholarly journals Study on Convolutional Neural Network to Detect COVID-19 from Chest X-Rays

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Azher Uddin ◽  
Bayazid Talukder ◽  
Mohammad Monirujjaman Khan ◽  
Atef Zaguia
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Close Contact ◽  
World Health ◽  
Contact Tracing ◽  
X Rays ◽  
Health Crisis ◽  
Gastrointestinal Problems ◽  
The World ◽  
Health Organization

The world is facing a pandemic due to the coronavirus disease 2019 (COVID-19), named as per the World Health Organization. COVID-19 is caused by the virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was initially discovered in late December 2019 in Wuhan, China. Later, the virus had spread throughout the world within a few months. COVID-19 has become a global health crisis because millions of people worldwide are affected by this fatal virus. Fever, dry cough, and gastrointestinal problems are the most common signs of COVID-19. The disease is highly contagious, and affected people can easily spread the virus to those with whom they have close contact. Thus, contact tracing is a suitable solution to prevent the virus from spreading. The method of identifying all persons with whom a COVID-19-affected patient has come into contact in the last 2 weeks is called contact tracing. This study presents an investigation of a convolutional neural network (CNN), which makes the test faster and more reliable, to detect COVID-19 from chest X-ray (CXR) images. Because there are many studies in this field, the designed model focuses on increasing the accuracy level and uses a transfer learning approach and a custom model. Pretrained deep CNN models, such as VGG16, InceptionV3, MobileNetV2, and ResNet50, have been used for deep feature extraction. The performance measurement in this study was based on classification accuracy. The results of this study indicate that deep learning can recognize SARS-CoV-2 from CXR images. The designed model provided 93% accuracy and 98% validation accuracy, and the pretrained customized models such as MobileNetV2 obtained 97% accuracy, InceptionV3 obtained 98%, and VGG16 obtained 98% accuracy, respectively. Among these models, InceptionV3 has recorded the highest accuracy.

scholarly journals Batik Classification using Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11416-11421
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Analysis ◽  
Convolutional Neural Network ◽  
Image Data ◽  
High Accuracy ◽  
Learning Method ◽  
Philosophy Of Life ◽  
Proposed Model

Batik is one of the Indonesian cultural heritages that has been recognized by the global community. Indonesian batik has a vast diversity in motifs that illustrate the philosophy of life, the ancestral heritage and also reflects the origin of batik itself. Because of the manybatik motifs, problems arise in determining the type of batik itself. Therefore, we need a classification method that can classify various batik motifs automatically based on the batik images. The technique of image classification that is used widely now is deep learning method. This technique has been proven of its capacity in identifying images in high accuracy. Architecture that is widely used for the image data analysis is Convolutional Neural Network (CNN) because this architecture is able to detect and recognize objects in an image. This workproposes to use the method of CNN and VGG architecture that have been modified to overcome the problems of classification of the batik motifs. Experiments of using 2.448 batik images from 5 classes of batik motifs showed that the proposed model has successfully achieved an accuracy of 96.30%.

scholarly journals BET degrader inhibits tumor progression and stem-like cell growth via Wnt/β-catenin signaling repression in glioma cells

2020 ◽  
Vol 11 (10) ◽  
Author(s):  
Tao Tian ◽  
Tongqi Guo ◽  
Wei Zhen ◽  
Jianjun Zou ◽  
Fuyong Li
Keyword(s):  
Stem Cell ◽  
Glioma Cells ◽  
World Health ◽  
Tumor Type ◽  
Cycle Arrest ◽  
Bet Inhibitors ◽  
Health Organization ◽  
The Brain

Abstract Based on their histological appearance, gliomas are a very common primary tumor type of the brain and are classified into grades, Grade I to Grade IV, of the World Health Organization. Treatment failure is due to the cancer stem cells (CSC) phenotype maintenance and self-renewal. BET degraders such as ZBC260 represents a novel class of BET inhibitors that act by inducing BET proteins degradation. This study explores the mode of action and effects of ZBC260 in vivo and in vitro against glioma. By inhibiting cell proliferation and inducting cell cycle arrest, the fact that glioma cell lines show sensitivity to ZBC260. Notably, ZBC260 targeted glioma without side effects in vivo. In addition, the stem cell-like properties of glioma cells were inhibited upon ZBC260 treatment. When the mechanism was examined, our findings indicated that Wnt/β-catenin pathway repression is required for ZBC260-induced stem cell-like properties and tumor growth suppression. In conclusion, the growth of tumors and stem cell-like properties were inhibited by ZBC260 via Wnt/β-catenin repression, which suggests ZBC260 as a potential therapeutic agent for glioma.

scholarly journals Vulnerable Road Users Classification of Smartphone Sensors Data Using Deep Transfer Learning

2020 ◽  
Author(s):  
Mohammed Elhenawy ◽  
Huthaifa Ashqar ◽  
Mahmoud Masoud ◽  
Mohammed Almannaa ◽  
Andry Rakotonirainy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
High Accuracy ◽  
Training Time ◽  
Road Users ◽  
Smartphone Sensors ◽  
Vulnerable Road Users ◽  
And Behavior

As the Autonomous Vehicle (AV) industry is rapidly advancing, classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes numerous training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227×227 images to be used for AlexNet and SqueezeNet; and constructing 224×224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

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