scholarly journals Personify Educational Assistance Application for Special Children using Deep Learning

2019 ◽  
Vol 8 (6S4) ◽  
pp. 502-506
Keyword(s):  
Learning Disabilities ◽  
Deep Learning ◽  
Facial Expressions ◽  
Exceptional Case ◽  
Training Dataset ◽  
Normal Children ◽  
Educational Assistance ◽  
Testing Dataset ◽  
Scanned Image ◽  
Special Children

Despite various Stand-alone educational assistance application for normal children but for the special children it was an exceptional case still, so this children with (Anxiety Disorder, ADHD, Learning Disabilities) find difficult to learn for long hours without getting distracted. A caretaker is needs to be with them at all the time in order to engage them in studying efficiently. Using this technology at its best, Deep Learning can be used to monitor the children when they are distracted and their attention can be drawn back by imposing volunteer distractions on the screen based on the concept of Face Recognition (in terms of facial expressions). The work has been implemented using python & OpenCV platform. By using this, The scanned image i.e. testing dataset is being compared to training dataset and thus emotion is predicted for incorporating with assisting component

scholarly journals Personify Educational Assistance Application for Special Children Using Deep Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 1609-1614
Keyword(s):  
Learning Disabilities ◽  
Deep Learning ◽  
Facial Expressions ◽  
Exceptional Case ◽  
Training Dataset ◽  
Normal Children ◽  
Educational Assistance ◽  
Testing Dataset ◽  
Scanned Image ◽  
Special Children

Despite various Stand-alone educational assistance application for normal children but for the special children it was an exceptional case still, so this children with (Anxiety Disorder, ADHD, Learning Disabilities) find difficult to learn for long hours without getting distracted. A caretaker is needs to be with them at all the time in order to engage them in studying efficiently. Using this technology at its best, Deep Learning can be used to monitor the children when they are distracted and their attention can be drawn back by imposing volunteer distractions on the screen based on the concept of Face Recognition (in terms of facial expressions). The work has been implemented using python & OpenCV platform. By using this, The scanned image i.e. testing dataset is being compared to training dataset and thus emotion is predicted for incorporating with assisting component

Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone

2021 ◽  
pp. bjophthalmol-2020-318107
Author(s):  
Kenichi Nakahara ◽  
Ryo Asaoka ◽  
Masaki Tanito ◽  
Naoto Shibata ◽  
Keita Mitsuhashi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Fundus Photography ◽  
Training Dataset ◽  
Mean Deviation ◽  
Fundus Camera ◽  
Deep Learning Algorithm ◽  
Testing Dataset ◽  
Advanced Glaucoma ◽  
Fundus Photographs

Background/aimsTo validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone.MethodsA training dataset consisting of 1364 colour fundus photographs with glaucomatous indications and 1768 colour fundus photographs without glaucomatous features was obtained using an ordinary fundus camera. The testing dataset consisted of 73 eyes of 73 patients with glaucoma and 89 eyes of 89 normative subjects. In the testing dataset, fundus photographs were acquired using an ordinary fundus camera and a smartphone. A deep learning algorithm was developed to diagnose glaucoma using a training dataset. The trained neural network was evaluated by prediction result of the diagnostic of glaucoma or normal over the test datasets, using images from both an ordinary fundus camera and a smartphone. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC).ResultsThe AROC with a fundus camera was 98.9% and 84.2% with a smartphone. When validated only in eyes with advanced glaucoma (mean deviation value < −12 dB, N=26), the AROC with a fundus camera was 99.3% and 90.0% with a smartphone. There were significant differences between these AROC values using different cameras.ConclusionThe usefulness of a deep learning algorithm to automatically screen for glaucoma from smartphone-based fundus photographs was validated. The algorithm had a considerable high diagnostic ability, particularly in eyes with advanced glaucoma.

scholarly journals Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model

2021 ◽  
Author(s):  
Yang Zhang ◽  
Siwa Chan ◽  
Jeon-Hor Chen ◽  
Kai-Ting Chang ◽  
Chin-Yao Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Breast Tissue ◽  
Ground Truth ◽  
Training Dataset ◽  
Breast Volume ◽  
Segmentation Method ◽  
Tissue Segmentation ◽  
Testing Dataset ◽  
The Mean

AbstractTo develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson’s correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.

scholarly journals The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective

2021 ◽  
Vol 8 ◽  
Author(s):  
Mohamed Elgendi ◽  
Muhammad Umer Nasir ◽  
Qunfeng Tang ◽  
David Smith ◽  
John-Paul Grenier ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Learning Algorithms ◽  
Training Dataset ◽  
Geometric Transformation ◽  
Detection Accuracy ◽  
X Rays ◽  
X Ray ◽  
Testing Dataset ◽  
The Impact

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with a χMcNemar′s statistic2=163.2 and a p-value of 2.23 × 10−37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.

scholarly journals Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet, and sunflower crops

2021 ◽  
Author(s):  
Etienne David ◽  
Gaëtan Daubige ◽  
François Joudelat ◽  
Philippe Burger ◽  
Alexis Comar ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Sugar Beet ◽  
Plant Development ◽  
Plant Density ◽  
Development Stage ◽  
Training Dataset ◽  
Testing Dataset ◽  
Weed Infestation ◽  
Rgb Images

1AbstractPlants density is a key information on crop growth. Usually done manually, this task can beneficiate from advances in image analysis technics. Automated detection of individual plants in images is a key step to estimate this density. To develop and evaluate dedicated processing technics, high resolution RGB images were acquired from UAVs during several years and experiments over maize, sugar beet and sunflower crops at early stages. A total of 16247 plants have been labelled interactively. We compared the performances of handcrafted method (HC) to those of deep-learning (DL). HC method consists in segmenting the image into green and background pixels, identifying rows, then objects corresponding to plants thanks to knowledge of the sowing pattern as prior information. DL method is based on the Faster RCNN model trained over 2/3 of the images selected to represent a good balance between plant development stage and sessions. One model is trained for each crop.Results show that DL generally outperforms HC, particularly for maize and sunflower crops. The quality of images appears mandatory for HC methods where image blur and complex background induce difficulties for the segmentation step. Performances of DL methods are also limited by image quality as well as the presence of weeds. An hybrid method (HY) was proposed to eliminate weeds between the rows using the rules used for the HC method. HY improves slightly DL performances in the case of high weed infestation. A significant level of variability of plant detection performances is observed between the several experiments. This was explained by the variability of image acquisition conditions including illumination, plant development stage, background complexity and weed infestation. We tested an active learning approach where few images corresponding to the conditions of the testing dataset were complementing the training dataset for DL. Results show a drastic increase of performances for all crops, with relative RMSE below 5% for the estimation of the plant density.

scholarly journals Epiretinal Membrane Detection at the Ophthalmologist Level using Deep Learning of Optical Coherence Tomography

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ying-Chih Lo ◽  
Keng-Hung Lin ◽  
Henry Bair ◽  
Wayne Huey-Herng Sheu ◽  
Chi-Sen Chang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Roc Curve ◽  
Epiretinal Membrane ◽  
Age Related Macular Degeneration ◽  
Training Dataset ◽  
Optical Coherence ◽  
Cross Sectional ◽  
Testing Dataset ◽  
Age Related

Abstract Purpose: Previous deep learning studies on optical coherence tomography (OCT) mainly focused on diabetic retinopathy and age-related macular degeneration. We proposed a deep learning model that can identify epiretinal membrane (ERM) in OCT with ophthalmologist-level performance. Design: Cross-sectional study. Participants: A total of 3,618 central fovea cross section OCT images from 1,475 eyes of 964 patients. Methods: We retrospectively collected 7,652 OCT images from 1,197 patients. From these images, 2,171 were normal and 1,447 were ERM OCT. A total of 3,141 OCT images was used as training dataset and 477 images as testing dataset. DL algorithm was used to train the interpretation model. Diagnostic results by four board-certified non-retinal specialized ophthalmologists on the testing dataset were compared with those generated by the DL model. Main Outcome Measures: We calculated for the derived DL model the following characteristics: sensitivity, specificity, F1 score and area under curve (AUC) of the receiver operating characteristic (ROC) curve. These were calculated according to the gold standard results which were parallel diagnoses of the retinal specialist. Performance of the DL model was finally compared with that of non-retinal specialized ophthalmologists. Results: Regarding the diagnosis of ERM in OCT images, the trained DL model had the following characteristics in performance: sensitivity: 98.7%, specificity: 98.0%, and F1 score: 0.945. The accuracy on the training dataset was 99.7% (95% CI: 99.4 - 99.9%), and for the testing dataset, diagnostic accuracy was 98.1% (95% CI: 96.5 - 99.1%). AUC of the ROC curve was 0.999. The DL model slightly outperformed the average non-retinal specialized ophthalmologists. Conclusions: An ophthalmologist-level DL model was built here to accurately identify ERM in OCT images. The performance of the model was slightly better than the average non-retinal specialized ophthalmologists. The derived model may play a role to assist clinicians to promote the efficiency and safety of healthcare in the future.

scholarly journals Predicting intraocular pressure using systemic variables or fundus photography with deep learning in a health examination cohort

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kaori Ishii ◽  
Ryo Asaoka ◽  
Takashi Omoto ◽  
Shingo Mitaki ◽  
Yuri Fujino ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intraocular Pressure ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Fundus Photography ◽  
Training Dataset ◽  
Support Vector ◽  
Testing Dataset ◽  
The Mean ◽  
Systemic Variables

AbstractThe purpose of the current study was to predict intraocular pressure (IOP) using color fundus photography with a deep learning (DL) model, or, systemic variables with a multivariate linear regression model (MLM), along with least absolute shrinkage and selection operator regression (LASSO), support vector machine (SVM), and Random Forest: (RF). Training dataset included 3883 examinations from 3883 eyes of 1945 subjects and testing dataset 289 examinations from 289 eyes from 146 subjects. With the training dataset, MLM was constructed to predict IOP using 35 systemic variables and 25 blood measurements. A DL model was developed to predict IOP from color fundus photographs. The prediction accuracy of each model was evaluated through the absolute error and the marginal R-squared (mR2), using the testing dataset. The mean absolute error with MLM was 2.29 mmHg, which was significantly smaller than that with DL (2.70 dB). The mR2 with MLM was 0.15, whereas that with DL was 0.0066. The mean absolute error (between 2.24 and 2.30 mmHg) and mR2 (between 0.11 and 0.15) with LASSO, SVM and RF were similar to or poorer than MLM. A DL model to predict IOP using color fundus photography proved far less accurate than MLM using systemic variables.

scholarly journals A Convolutional Neural Network Deep Learning Model Trained on CD Ulcers Images Accurately Identifies NSAID Ulcers

2021 ◽  
Vol 8 ◽  
Author(s):  
Eyal Klang ◽  
Uri Kopylov ◽  
Brynjulf Mortensen ◽  
Anders Damholt ◽  
Shelly Soffer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Normal Mucosa ◽  
Research Field ◽  
Receiver Operating Curve ◽  
Training Dataset ◽  
Future Design ◽  
Testing Dataset ◽  
Inflammatory Drugs ◽  
Gastrointestinal Adverse Events

Background and Study Aims: Deep learning (DL) for video capsule endoscopy (VCE) is an emerging research field. It has shown high accuracy for the detection of Crohn's disease (CD) ulcers. Non-steroidal anti-inflammatory drugs (NSAIDS) are commonly used medications. In the small bowel, NSAIDs may cause a variety of gastrointestinal adverse events including NSAID-induced ulcers. These ulcers are the most important differential diagnosis for small bowel ulcers in patients evaluated for suspected CD. We evaluated a DL network that was trained using CD VCE ulcer images and evaluated its performance for NSAID ulcers.Patients and Methods: The network was trained using CD ulcers and normal mucosa from a large image bank created from VCE of diagnosed CD patients. NSAIDs-induced enteropathy images were extracted from the prospective Bifidobacterium breve (BIf95) trial dataset. All images were acquired from studies performed using PillCam SBIII. The area under the receiver operating curve (AUC) was used as a metric. We compared the network's AUC for detecting NSAID ulcers to that of detecting CD ulcers.Results: Overall, the CD training dataset included 17,640 CE images. The NSAIDs testing dataset included 1,605 CE images. The DL network exhibited an AUC of 0.97 (95% CI 0.97–0.98) for identifying images with NSAID mucosal ulcers. The diagnostic accuracy was similar to that obtained for CD related ulcers (AUC 0.94–0.99).Conclusions: A network trained on VCE CD ulcers similarly identified NSAID findings. As deep learning is transforming gastrointestinal endoscopy, this result should be taken into consideration in the future design and analysis of VCE deep learning applications.

scholarly journals Feature Extraction aligned Email Classification based on Imperative Sentence Selection through Deep Learning

2021 ◽  
Vol 3 (1) ◽  
pp. 93-114
Author(s):  
Nashit Ali ◽  
◽  
Anum Fatima ◽  
Hureeza Shahzadi ◽  
Aman Ullah ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Experimental Result ◽  
Training Dataset ◽  
Research Use ◽  
Huge Amount ◽  
Testing Dataset ◽  
Result Show ◽  
Email Classification

Most commonly used channel for communication among peoples is emails. In this era where everyone is so busy in their routine and work, it is very difficult to check all email when one receives huge amount of emails. Previous research has done work on email categorization in which they have mostly done spam filtration. The problem with spam filtration is that sometimes person mistakenly mark an important email received from high authority as spam and according to previous research, this email will be filtered as spam that can cause a great threat for job of an employee. In this research, we are introducing a methodology which classifies email text into three categories i.e. order, request and general on basis of imperative sentences. This research use Word2Wec for words conversion into vector and use two approaches of deep learning i.e. Convolutional neural network and Recurrent neural network for email classification. We conduct experiment on Dataset collected from Personal Gmail account and Enron which consists of 1000 emails. The experiment result show that RNN gives better accuracy than CNN. We also compare our methods with previously used method Fuzzy ANN results and Our proposed methods CNN and RNN gives better results than Fuzzy ANN. This research has also included different experimental result in which CNN and RNN applied on different ratios of training and testing dataset. These experiment show that increasing in the ratio of training dataset results in increasing accuracy of algorithm.

scholarly journals A Deep Learning Model to Classify and Detect Brain Abnormalities in Portable Microwave Based Imaging System

2021 ◽  
Author(s):  
Amran Hossain ◽  
Mohammad Tariqul Islam ◽  
Ali F. Almutairi
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Malignant Tumors ◽  
Training Dataset ◽  
Detection Accuracy ◽  
Automated Classification ◽  
Brain Abnormalities ◽  
Detection Model ◽  
Deep Model ◽  
Testing Dataset

Abstract Automated classification and detection of brain abnormalities like tumors from microwave head images is essential for investigating and monitoring disease progression. This paper presents the automatic classification and detection of human brain abnormalities through the deep learning-based YOLOv5 model in microwave head images. The YOLOv5 is a faster object detection model, which has a less computational architecture with high accuracy. At the beginning, the backscattered signals are collected from the implemented 3D wideband nine antennas array-based microwave head imaging (MWHI) system, where one antenna operates as a transmitter and the remaining eight antennas operate as receivers. In this research, fabricated tissue-mimicking head phantom with a benign and malignant tumor as brain abnormalities, which is utilized in MWHI system. Afterwards, the M-DMAS (modified-delay-multiply-and-sum) imaging algorithm is applied on the post-processed scattering parameters to reconstruct head regions image with 640×640 pixels. Three hundred sample images are collected, including benign and malignant tumors from various locations in head regions by the MWHI system. Later, the images are preprocessed and augmented to create a final image dataset containing 3600 images, and then used for training, validation, and testing the YOLOv5 model. Subsequently, 80% of images are utilized for training, and 20% are used for testing the model. Then from the 80% training dataset, 20% is utilized for validation to avoid overfitting. The brain abnormalities classification and detection performances with various datasets are investigated by the YOLOv5s, YOLOv5m, and YOLOv5l models of YOLOv5. It is investigated that the YOLOv5l model showed the best result for abnormalities classification and detection compared to other models. However, the achieved training accuracy, validation loss, precision, recall, F1-score, training and validation classification loss, and mean average precision (mAP) are 99.84%, 9.38%, 93.20%, 94.80%, 94.01%, 0.004, 0.0133, and 96.20% respectively for the YOLOv5l model, which ensures the better classification and detection accuracy of the model. Finally, a testing dataset with different scenarios is evaluated through the three versions of the YOLOv5 model, and conclude that brain abnormalities classification and detection with location are successfully classified and detected. Thus, the deep model is applicable in the portable MWHI system.

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