Abstract WP437: Where Are We Located Now With Artificial Neural Network? Application of Deep Learning for the Development of Culturally Sensitive Social Support Interventions for Dementia Caregivers

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Sunmoo Yoon ◽  
Dante Tipiani ◽  
Peter Broadwell ◽  
Jose Luchsigner
Keyword(s):  
Social Support ◽  
African American ◽  
Deep Learning ◽  
Vascular Dementia ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Culturally Sensitive ◽  
Health Science ◽  
Learning Methods ◽  
Dementia Caregivers

Introduction: Little is known about culturally sensitive interventions for Hispanic and African American family caregivers for persons living with vascular dementia. Applications of deep-learning neural networks have expanded rapidly in recent years, from the first uses in character recognition (1989) to the high-profile AlphaGo (2016) from Google DeepMind. The purpose of this study is to visualize topics from health literature that use deep learning, exploring the techniques’ ability to provide a foundation for developing culturally sensitive Twitter-based social support interventions for Hispanic and African American dementia caregivers. Methods: The corpus of deep learning literature was extracted from 506 studies mentioning deep learning from PubMed library. Text mining was conducted using AutoMap to identify topics and semantic relations from the corpus. Results were visualized as network clusters of frequently applied topics and isolated topics. Results: Literature applying deep learning are limited to 1) imaging and radiology, 2) genomics, 3) cancer (N=506 studies). Topics on dementia and drug discovery were identified as an emerging area for the adaptation of deep learning techniques (Figure 1). Only seven journals in the PubMed library were identified as having published more than 10 studies applying deep learning methods. Conclusion: Despite its popularity in art and science research, deep learning is at an early stage of adoption in health science, largely focused on limited topics, e.g., imaging and disease diagnosis. Applying deep learning methods may provide insights for developing culturally sensitive Twitter-based interventions for Hispanic and African American dementia caregivers. Implementing education on applying deep learning to imaging and vascular-dementia prediction may be a good start to adapt, deepen and broaden the scope of health analytics to improve vascular dementia patients’ health outcomes.

Diagnosing breast cancer tumors using stacked ensemble model

2021 ◽  
pp. 1-9
Author(s):  
Ahmet Haşim Yurttakal ◽  
Hasan Erbay ◽  
Türkan İkizceli ◽  
Seyhan Karaçavuş ◽  
Cenker Biçer
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Early Stage ◽  
False Negative ◽  
Gradient Boosting ◽  
Physical Sign ◽  
Ensemble Model ◽  
Learning Methods ◽  
Dce Mri

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

scholarly journals Deep Learning for Caries Detection and Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1672
Author(s):  
Luya Lian ◽  
Tianer Zhu ◽  
Fudong Zhu ◽  
Haihua Zhu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Validation Dataset ◽  
Reference Dataset ◽  
Dice Coefficient ◽  
Learning Methods ◽  
Test Dataset ◽  
The Impact ◽  
Caries Lesions

Objectives: Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. Methods: A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then established from the reference dataset. A convolutional neural network, called nnU-Net, was applied to detect caries lesions, and DenseNet121 was applied to classify the lesions according to their depths (dentin lesions in the outer, middle, or inner third D1/2/3 of dentin). The performance of the test dataset in the trained nnU-Net and DenseNet121 models was compared with the results of six expert dentists in terms of the intersection over union (IoU), Dice coefficient, accuracy, precision, recall, negative predictive value (NPV), and F1-score metrics. Results: nnU-Net yielded caries lesion segmentation IoU and Dice coefficient values of 0.785 and 0.663, respectively, and the accuracy and recall rate of nnU-Net were 0.986 and 0.821, respectively. The results of the expert dentists and the neural network were shown to be no different in terms of accuracy, precision, recall, NPV, and F1-score. For caries depth classification, DenseNet121 showed an overall accuracy of 0.957 for D1 lesions, 0.832 for D2 lesions, and 0.863 for D3 lesions. The recall results of the D1/D2/D3 lesions were 0.765, 0.652, and 0.918, respectively. All metric values, including accuracy, precision, recall, NPV, and F1-score values, were proven to be no different from those of the experienced dentists. Conclusion: In detecting and classifying caries lesions on dental panoramic radiographs, the performance of deep learning methods was similar to that of expert dentists. The impact of applying these well-trained neural networks for disease diagnosis and treatment decision making should be explored.

scholarly journals A Comparative Analysis of Early Stage Diabetes Prediction using Machine Learning and Deep Learning Approach

2021 ◽  
Author(s):  
Md. Abu Rumman Refat ◽  
Md Al Amin ◽  
Chetna Kaushal ◽  
Mst. Nilufa Yeasmin ◽  
Md Khairul Islam
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Blood Sugar ◽  
Performance Metrics ◽  
Early Stage ◽  
Health Science ◽  
Classification Algorithms ◽  
Early Diabetes ◽  
Uncontrolled Diabetes

Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. The common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms. indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0%, while the rest of the algorithms were over 90.0% accurate.<br>

scholarly journals A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images

2021 ◽  
Vol 11 (1) ◽  
pp. 844
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Early Stage ◽  
Diagnostic Technique ◽  
Disease Diagnosis ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Low Sensitivity ◽  
Deep Learning Model

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

scholarly journals Deep Learning Methods On Neutron Scattering Data

2020 ◽  
Vol 225 ◽  
pp. 01004
Author(s):  
Guanghan Song ◽  
Lionel Porcar ◽  
Martin Boehm ◽  
Franck Cecillon ◽  
Charles Dewhurst ◽  
...  
Keyword(s):  
Experimental Data ◽  
Deep Learning ◽  
Neutron Scattering ◽  
Human Performance ◽  
Early Stage ◽  
Scientific Information ◽  
Scattering Data ◽  
Learning Methods ◽  
Structure Geometry ◽  
Instrument Configuration

Recently, by using deep learning methods, a computer is able to surpass or come close to matching human performance on image analysis and recognition. This advanced methods could also help extracting features from neutron scattering experimental data. Those data contain rich scientific information about structure and dynamics of materials under investigation. Deep learning could help researchers better understand the link between experimental data and materials properties. Moreover,it could also help to optimize neutron scattering experiment by predicting the best possible instrument configuration. Among all possible experimental methods, we begin our study on the small-angle neutron scattering (SANS) data and by predicting the structure geometry of the sample material at an early stage. This step is a keystone to predict the experimental parameters to properly setup the instrument as well as the best measurement strategy. In this paper, we propose to use transfer learning to retrain a convolutional neural networks (CNNs) based pre rained model to adapt the scattering images classification, which could predict the structure of the materials at an early stage in the SANS experiment. This deep neural network is trained and validated on simulated database, and tested on real scattering images.

A Church-based Culturally Sensitive Physical Activity Intervention in African American Women

2020 ◽  
pp. 019394592096140
Author(s):  
Elicia S. Collins ◽  
Susan W. Buchholz ◽  
Joan Cranford ◽  
Megan A. McCrory
Keyword(s):  
Physical Activity ◽  
Social Support ◽  
African American ◽  
Pilot Study ◽  
African American Women ◽  
Self Regulation ◽  
Culturally Sensitive ◽  
Convenience Sample ◽  
American Women ◽  
Study Results

The purpose of this pilot study was to test a church-based, culturally sensitive, six-week intervention called GET FIT DON’T QUIT. The intervention aimed to increase knowledge and change beliefs about physical activity, and to improve social facilitation to increase self-regulation, in order to promote physical activity in African-American women. A two-group pretest/posttest, quasi-experimental design was conducted in a convenience sample ( N = 37) of African-American women. Participants were randomly assigned to the intervention or control group by church affiliation. The six-week intervention consisted of teaching and roundtable discussions, and email reminders to be physically active. There were significant differences ( p < .05) in the level of self-efficacy, self-regulation, and friend social support. There were no significant differences in knowledge of physical activity guidelines, beliefs, and family social support. These pilot study results suggested that multiple factors are associated with physical activity engagement in African-American women.

scholarly journals Development of Decision Support System for Early Stage Diabetes Diagnosis Using Machine Learning and Deep Learning Methods: A Comprehensive Analysis

2021 ◽  
Vol 4 (1) ◽  
pp. 186-194
Author(s):  
Caglar Gurkan ◽  
Sude Kozalioglu ◽  
Merih Palandoken
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Naive Bayes ◽  
Early Stage ◽  
Comprehensive Analysis ◽  
Diabetes Diagnosis ◽  
Learning Methods

Yaygın olarak görülen hastalıklardan biri olan diyabetin prevalansı her yıl artmaktadır. Diyabet hastalığı erken teşhis edilmezse kalp ve damar hastalıklarına, böbrek hastalığına, körlüğe, sinir hasarlarına, felce ve organ yetmezliklerine neden olabilir. Ayrıca bu diyabet hastaları için yapılacak sağlık harcamalarının da 2040 yılında 802 milyon dolar olacağı tahmin edilmektedir. Bu durumlar göz önünde bulundurulduğunda diyabet tanısı için yapılacak çalışmalar oldukça önemlidir. Bu çalışmada, diyabet tanısı için bir karar destek sistemi geliştirmek amacıyla karar ağaçları, k-en yakın komşu, lojistik regresyon, Naive Bayes, rastgele orman, destek vektör makineleri gibi makine öğrenmesi ve çok katmanlı algılayıcı (ÇKA), evrişimli sinir ağları (ESA), tekrarlayan sinir ağları (RNN) tasarımları olan Basit RNN, Uzun Kısa Dönem Bellek Ağları (LSTM), Geçitli Tekrarlayan Birim (GRU), İki Yönlü Uzun Kısa Dönem Bellek Ağları (BiLSTM), İki Yönlü Geçitli Tekrarlayan Birim (BiGRU), ESA ve RNN hibrit modelleri olan ESA+Simple RNN, ESA+LSTM, ESA+GRU, ESA+BiLSTM ve ESA+BiGRU gibi derin öğrenme yöntemleri kullanılmıştır. Makine öğrenmesi tabanlı sınıflandırıcılar içerisinde en yüksek sınıflandırma performansını %98.10 doğruluk oranı ve %98.00 F1- skoru ile DVM elde etmiştir. Derin öğrenme tabanlı sınıflandırıcılar içerisinde en yüksek sınıflandırma performansını %99.50 doğruluk oranı ve %99.30 F1- skoru ile ESA+BiGRU hibrit modeli elde etmiştir. Genel analizde ise, derin öğrenme tabanlı sınıflandırıcıların makine öğrenmesi tabanlı sınıflandırıcılara göre daha iyi performans göstermiştir. Ek olarak CNN ve RNN tasarımlarının hibrit modelleri, yalın modellere göre daha iyi sınıflandırma performansına sahiptir.

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