scholarly journals A Deep Learning Based Prediction of Arabic Manuscripts Handwriting Style

2020 ◽  
Vol 17 (5) ◽  
pp. 702-712
Author(s):  
Manal Khayyat ◽  
Lamiaa Elrefaei
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Evaluation Metrics ◽  
The Internet ◽  
Promising Technology ◽  
Deep Learning Model

With the increasing amounts of existing unorganized images on the internet today and the necessity to use them efficiently in various types of applications. There is a critical need to discover rigid models that can classify and predict images successfully and instantaneously. Therefore, this study aims to collect Arabic manuscripts images in a dataset and predict their handwriting styles using the most powerful and trending technologies. There are many types of Arabic handwriting styles, including Al-Reqaa, Al-Nask, Al-Thulth, Al-Kufi, Al-Hur, Al-Diwani, Al-Farsi, Al-Ejaza, Al-Maghrabi, Al-Taqraa, etc. However, the study classified the collected dataset images according to the handwriting styles and focused on only six types of handwriting styles that existed in the collected Arabic manuscripts. To reach our goal, we applied the MobileNet pre-trained deep learning model on our classified dataset images to automatically capture and extract the features from them. Afterward, we evaluated the performance of the developed model by computing its recorded evaluation metrics. We reached that MobileNet convolutional neural network is a promising technology since it reached 0.9583 as the highest recorded accuracy and 0.9633 as the average F-score

scholarly journals A robust and interpretable, end-to-end deep learning model for cytometry data

2020 ◽  
Author(s):  
Zicheng Hu ◽  
Alice Tang ◽  
Jaiveer Singh ◽  
Sanchita Bhattacharya ◽  
Atul J. Butte
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Model ◽  
Deep Convolutional Neural Network ◽  
Significant Information ◽  
End To End ◽  
Deep Learning Model

AbstractCytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Traditional approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large CyTOF studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model and identified a CD27-CD94+ CD8+ T cell population significantly associated with latent CMV infection. Finally, we provide a tutorial for creating, training and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (github.com/hzc363/DeepLearningCyTOF).

scholarly journals Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2012
Author(s):  
Jiameng Gao ◽  
Chengzhong Liu ◽  
Junying Han ◽  
Qinglin Lu ◽  
Hengxing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Wheat Variety ◽  
Learning Model ◽  
Wheat Cultivars ◽  
Learning Methods ◽  
Wheat Varieties ◽  
Deep Learning Model

Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production.

scholarly journals Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data

10.2196/24762 ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. e24762
Author(s):  
Hyun-Lim Yang ◽  
Chul-Woo Jung ◽  
Seong Mi Yang ◽  
Min-Soo Kim ◽  
Sungho Shim ◽  
...  
Keyword(s):  
Neural Network ◽  
Cardiac Output ◽  
Deep Learning ◽  
Retrospective Study ◽  
Arterial Pressure ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Model ◽  
Learning Technique ◽  
Deep Learning Model

Background Arterial pressure-based cardiac output (APCO) is a less invasive method for estimating cardiac output without concerns about complications from the pulmonary artery catheter (PAC). However, inaccuracies of currently available APCO devices have been reported. Improvements to the algorithm by researchers are impossible, as only a subset of the algorithm has been released. Objective In this study, an open-source algorithm was developed and validated using a convolutional neural network and a transfer learning technique. Methods A retrospective study was performed using data from a prospective cohort registry of intraoperative bio-signal data from a university hospital. The convolutional neural network model was trained using the arterial pressure waveform as input and the stroke volume (SV) value as the output. The model parameters were pretrained using the SV values from a commercial APCO device (Vigileo or EV1000 with the FloTrac algorithm) and adjusted with a transfer learning technique using SV values from the PAC. The performance of the model was evaluated using absolute error for the PAC on the testing dataset from separate periods. Finally, we compared the performance of the deep learning model and the FloTrac with the SV values from the PAC. Results A total of 2057 surgical cases (1958 training and 99 testing cases) were used in the registry. In the deep learning model, the absolute errors of SV were 14.5 (SD 13.4) mL (10.2 [SD 8.4] mL in cardiac surgery and 17.4 [SD 15.3] mL in liver transplantation). Compared with FloTrac, the absolute errors of the deep learning model were significantly smaller (16.5 [SD 15.4] and 18.3 [SD 15.1], P<.001). Conclusions The deep learning–based APCO algorithm showed better performance than the commercial APCO device. Further improvement of the algorithm developed in this study may be helpful for estimating cardiac output accurately in clinical practice and optimizing high-risk patient care.

scholarly journals A Novel Deep Convolutional Neural Network for Tuberculosis Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 1135-1139
Author(s):  
Syed Farhan Hyder Abidi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mycobacterium Tuberculosis ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Model

India accounts for the world’s largest number of cases in TB, with 2.8 million cases annually, and accounts for more than a quarter of the global TB burden. Tuberculosis (TB) is caused by the bacterium (Mycobacterium tuberculosis) which most commonly affects the lungs. TB is transmitted from person to person through the air. When people with TB cough, sneeze or spit, the germs are propelled into the air. This paper showcases a methodology which uses a Deep Learning Model (dCNN) for the detection of Tuberculosis in the lungs. The accuracy obtained by the methods for the model is desirable and dependable, which is increasingly productive in contrast to the accuracy shown by other neural networks.

Handwritten Text Recognition and Conversion Using Convolutional Neural Network (CNN) Based Deep Learning Model

2021 ◽  
Author(s):  
Jebaveerasingh Jebadurai ◽  
Immanuel Johnraja Jebadurai ◽  
Getzi Jeba Leelipushpam Paulraj ◽  
Sushen Vallabh Vangeepuram
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Text Recognition ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Deep Learning Model

scholarly journals Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256500
Author(s):  
Maleika Heenaye-Mamode Khan ◽  
Nazmeen Boodoo-Jahangeer ◽  
Wasiimah Dullull ◽  
Shaista Nathire ◽  
Xiaohong Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Learning Rate ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Model

The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.

scholarly journals A robust and interpretable end-to-end deep learning model for cytometry data

2020 ◽  
Vol 117 (35) ◽  
pp. 21373-21380
Author(s):  
Zicheng Hu ◽  
Alice Tang ◽  
Jaiveer Singh ◽  
Sanchita Bhattacharya ◽  
Atul J. Butte
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Model ◽  
Deep Convolutional Neural Network ◽  
Significant Information ◽  
End To End ◽  
Deep Learning Model

Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large cytometry by time-of-flight mass spectrometry or mass cytometry (CyTOF) studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model. We were able to identify a CD27- CD94+ CD8+ T cell population significantly associated with latent CMV infection, confirming the findings in previous studies. Finally, we provide a tutorial for creating, training, and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (https://github.com/hzc363/DeepLearningCyTOF).

On The Effect Of Decomposition Granularity On DeTraC For COVID-19 Detection Using Chest X-Ray Images

2021 ◽  
Author(s):  
Nicole P. Mugova ◽  
Mohammed M. Abdelsamea ◽  
Mohamed M. Gaber
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Deep Convolutional Neural Network ◽  
Experimental Results ◽  
Number Of Clusters ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

Covid-19 is a growing issue in society and there is a need for resources to manage the disease. This paper looks at studying the effect of class decomposition in our previously proposed deep Convolutional Neural Network, called DeTraC (Decompose, Transfer and Compose). DeTraC has the ability to robustly detect and predict Covid-19 from chest X-ray images. The experimental results showed that changing the number of clusters affected the performance of DeTraC and influenced the accuracy of the model. As the number of clusters increased, the accuracy decreased for the shallow tuning mode but increased for the deep tuning mode. This shows the importance of using suitable hyperparameter settings in order to get the best results from a deep learning model. The highest accuracy obtained, in this study, was 98.33% from the deep tuning model.

scholarly journals Expiry Date Digit Recognition using Convolutional Neural Network

2021 ◽  
Vol 5 (1) ◽  
pp. 85-88
Author(s):  
Tareq Khan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Learning Model ◽  
Expiry Date ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Deep Learning Model

The expiry dates printed on the merchandise have a distinct background, font, alignment, and color in comparison with the available handwritten digit datasets. In this paper, an expiry date dataset is used, and also a convolutional neural network (CNN) model is proposed to recognize expiry dates out of images. This model may be employed together with our previously proposed smart expiry architecture to get an automated notification to the smartphone for the foods which are expiring soon. The suggested deep learning model is tested and has a classification accuracy of 90%.

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