scholarly journals A deep learning solution for NAFLD screening diagnosis

2020 ◽  
Author(s):  
Noor Ayesha ◽  
Saleha Yurf ◽  
Syed Mohammad Mehmood Abbas ◽  
Ali Haider Bangash ◽  
Adil Baloch ◽  
...  
Keyword(s):  
Deep Learning ◽  
Global Health ◽  
Health Resources ◽  
Learning Model ◽  
High Accuracy ◽  
Abdominal Ultrasonography ◽  
Monetary Cost ◽  
Deep Learning Model ◽  
Very High

NAFLD is reported to be the only hepatic ailment increasing in itsprevalence concurrently with both; obesity & T2DM. In the wake of a massivestrain on global health resources due to COVID 19 pandemic, NAFLD is boundto be neglected & shelved. Abdominal ultrasonography is done for NAFLDscreening diagnosis which has a high monetary cost associated with it. Wepresent a deep learning model that requires only easy-to-measureanthropometric measures for coming up with a screening diagnosis for NAFLDwith very high accuracy. Further studies are suggested to validate thegeneralization of the presented model.

scholarly journals COMPACT AND HYBRID FEATURE DESCRIPTION FOR BUILDING EXTRACTION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 643-646
Author(s):  
Z. Li ◽  
Y. Liu ◽  
Y. Hu ◽  
P. Li ◽  
Y. Ding
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Learning Model ◽  
High Accuracy ◽  
Building Extraction ◽  
Image Patch ◽  
Feature Description ◽  
Building Roof ◽  
Binary Description ◽  
Deep Learning Model

Building extraction in aerial orthophotos is crucial for various applications. Currently, deep learning has been shown to be successful in addressing building extraction with high accuracy and high robustness. However, quite a large number of samples is required in training a classifier when using deep learning model. In order to realize accurate and semi-interactive labelling, the performance of feature description is crucial, as it has significant effect on the accuracy of classification. In this paper, we bring forward a compact and hybrid feature description method, in order to guarantees desirable classification accuracy of the corners on the building roof contours. The proposed descriptor is a hybrid description of an image patch constructed from 4 sets of binary intensity tests. Experiments show that benefiting from binary description and making full use of color channels, this descriptor is not only computationally frugal, but also accurate than SURF for building extraction.

An Improved Deep-Learning Model for Road Extraction from Very-High-Resolution Remote Sensing Images

2021 ◽  
Author(s):  
Wangyao Shen ◽  
Yunping Chen ◽  
Yuanlei Cheng ◽  
Kangzhuo Yang ◽  
Xiang Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Model ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Deep Learning Model ◽  
Very High

scholarly journals Method of Profanity Detection Using Word Embedding and LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
MoungHo Yi ◽  
MyungJin Lim ◽  
Hoon Ko ◽  
JuHyun Shin
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
High Accuracy ◽  
Verbal Abuse ◽  
Word Embedding ◽  
Internet Users ◽  
Starting Point ◽  
Deep Learning Model ◽  
Discrimination Methods

With the rising number of Internet users, there has been a rapid increase in cyberbullying. Among the types of cyberbullying, verbal abuse is emerging as the most serious problem, for preventing which profanity is being identified and blocked. However, users employ words cleverly to avoid blocking. With the existing profanity discrimination methods, deliberate typos and profanity using special characters can be discriminated with high accuracy. However, as they cannot grasp the meaning of the words and the flow of sentences, standard words such as “Sibaljeom (starting point, a Korean word that sounds similar to a swear word)” and “Saekkibalgalag (little toe, a Korean word that sounds similar to another swear word)” are less accurately discriminated. Therefore, in order to solve this problem, this study proposes a method of discriminating profanity using a deep learning model that can grasp the meaning and context of words after separating Hangul into the onset, nucleus, and coda.

scholarly journals On the Relation of Gene Essentiality to Intron Structure: A Computational and Deep Learning Approach

2020 ◽  
Author(s):  
Ethan Schonfeld ◽  
Edward Vendrow ◽  
Joshua Vendrow ◽  
Elan Schonfeld
Keyword(s):  
Deep Learning ◽  
Practical Importance ◽  
Intron Loss ◽  
Learning Model ◽  
High Accuracy ◽  
Essential Genes ◽  
Enhancer Activity ◽  
Gene Essentiality ◽  
Deep Learning Model ◽  
Nonessential Genes

AbstractIdentification and study of human-essential genes has become of practical importance with the realization that disruption or loss of nearby essential genes can introduce latent-vulnerabilities to cancer cells. Essential genes have been studied by copy-number-variants and deletion events, which are associated with introns. The premise of our work is that introns of essential genes have characteristic properties that are distinct from the introns of nonessential genes. We provide support for the existence of characteristic properties by training a deep learning model on introns of essential and nonessential genes and demonstrated that introns alone can be used to classify essential and nonessential genes with high accuracy (AUC of 0.846). We further demonstrated that the accuracy of the same deep-learning model limited to first introns will perform at an increased level, thereby demonstrating the critical importance of introns and particularly first introns in gene essentiality. Using a computational approach, we identified several novel properties of introns of essential genes, finding that their structure protects against deletion and intron-loss events, and that these traits are especially centered on the first intron. We showed that GC density is increased in the first introns of essential genes, allowing for increased enhancer activity, protection against deletions, and improved splice-site recognition. Furthermore, we found that first introns of essential genes are of remarkably smaller size than their nonessential counterparts, and to protect against common 3’ end deletion events, essential genes carry an increased number of (smaller) introns. To demonstrate the importance of the seven features we identified, we trained a feature–based model using only information from these features and achieved high accuracy (AUC of 0.787).

scholarly journals Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 372
Author(s):  
Muhannad Faleh Alanazi ◽  
Muhammad Umair Ali ◽  
Shaik Javeed Hussain ◽  
Amad Zafar ◽  
Mohammed Mohatram ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Brain Mri ◽  
Learning Model ◽  
High Accuracy ◽  
Resonance Imaging ◽  
Deep Learning Model ◽  
The Brain

With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.

scholarly journals Leveraging AutoML to provide NAFLD screening diagnosis: Proposed machine learning models

2020 ◽  
Author(s):  
Ali Haider Bangash
Keyword(s):  
Machine Learning ◽  
Global Health ◽  
Health Resources ◽  
Learning Models ◽  
Anthropometric Measures ◽  
Abdominal Ultrasonography ◽  
Monetary Cost ◽  
Web Platform ◽  
Machine Learning Models

AbstractNAFLD is reported to be the only hepatic ailment increasing in its prevalence concurrently with both; obesity & T2DM. In the wake of a massive strain on global health resources due to COVID 19 pandemic, NAFLD is bound to be neglected & shelved. Abdominal ultrasonography is done for NAFLD screening diagnosis which has a high monetary cost associated with it. We utilized MLjar, an autoML web platform, to propose machine learning models that require no coding whatsoever & take in only easy-to-measure anthropometric measures for coming up with a screening diagnosis for NAFLD with considerably high AUC. Further studies are suggested to validate the generalization of the presented models.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

2020 ◽  
Vol 197 ◽  
pp. 105674
Author(s):  
Dingding Yu ◽  
Kaijie Zhang ◽  
Lingyan Huang ◽  
Bonan Zhao ◽  
Xiaoshan Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Peripherally Inserted Central Catheter ◽  
Central Catheter ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model
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