A novel compressive sensing with deep learning–based disease diagnosis model for smart wearable healthcare devices

Fruit tree diseases have a great influence on agricultural production. Artificial intelligence technologies have been used to help fruit growers identify fruit tree diseases in a timely and accurate way. In this study, a dataset of 10,000 images of pear black spot, pear rust, apple mosaic, and apple rust was used to develop the diagnosis model. To achieve better performance, we developed three kinds of ensemble learning classifiers and two kinds of deep learning classifiers, validated and tested these five models, and found that the stacking ensemble learning classifier outperformed the other classifiers with the accuracy of 98.05% on the validation dataset and 97.34% on the test dataset, which hinted that, with the small- and middle-sized dataset, stacking ensemble learning classifiers may be used as cost-effective alternatives to deep learning models under performance and cost constraints.

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Optimal Deep Learning based Classification Model for Mitral Valve Diagnosis System

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c6530.049420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 315-321

Keyword(s):

Neural Network ◽

Deep Learning ◽

Mitral Valve ◽

Edge Detection ◽

Disease Diagnosis ◽

Classification Model ◽

Validation Parameters ◽

Effective Operation ◽

Diagnosis Model

In present days, the domain of mitral valve (MV) diagnosis so common due to the changing lifestyle in day to day life. The increased number of MV disease necessitates the development of automated disease diagnosis model based on segmentation and classification. This paper makes use of deep learning (DL) model to develop a MV classification model to diagnose the severity level. For the accurate classification of ML, this paper applies the DL model called convolution neural network (CNN-MV) model. And, an edge detection based segmentation model is also applied which will helps to further enhance the performance of the classifier. Due to the non-availability of MV dataset, we have collected a MV dataset of our own from a total of 211 instances. A set of three validation parameters namely accuracy, sensitivity and specificity are applied to indicate the effective operation of the CNN-MV model. The obtained simulation outcome pointed out that the presented CNN-MV model functions as an appropriate tool for MV diagnosis

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Deep Learning in Disease Diagnosis: Models and Datasets

Current Bioinformatics ◽

10.2174/1574893615999201002124021 ◽

2020 ◽

Vol 15 ◽

Author(s):

Deeksha Saxena ◽

Mohammed Haris Siddiqui ◽

Rajnish Kumar

Keyword(s):

Biological Sciences ◽

Machine Learning ◽

Deep Learning ◽

Disease Diagnosis ◽

Learning Models ◽

Data Types ◽

Related Data ◽

Abstract Level ◽

Experimental Validations ◽

Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

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Deep learning for intelligent diagnosis in thyroid scintigraphy

Journal of International Medical Research ◽

10.1177/0300060520982842 ◽

2021 ◽

Vol 49 (1) ◽

pp. 030006052098284

Author(s):

Tingting Qiao ◽

Simin Liu ◽

Zhijun Cui ◽

Xiaqing Yu ◽

Haidong Cai ◽

...

Keyword(s):

Deep Learning ◽

Disease Diagnosis ◽

Kappa Coefficient ◽

Diagnostic Assessment ◽

First Year ◽

Thyroid Scintigraphy ◽

Diagnostic Ability ◽

The Third ◽

Classification Time ◽

Better Than

Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

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Crop Disease Diagnosis using Deep Learning Models

2020 Global Conference on Wireless and Optical Technologies (GCWOT) ◽

10.1109/gcwot49901.2020.9391605 ◽

2020 ◽

Author(s):

Waleej Haider ◽

Aqeel Ur Rehman ◽

Ahmed Maqsood ◽

Syed Zurain Javed

Keyword(s):

Deep Learning ◽

Disease Diagnosis ◽

Learning Models ◽

Crop Disease

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Kashin-Beck Disease Diagnosis Based on Deep Learning from Hand X-ray Images

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105919 ◽

2020 ◽

pp. 105919

Author(s):

Jinyuan Dang ◽

Hu Li ◽

Kai Niu ◽

Zhiyuan Xu ◽

Jianhao Lin ◽

...

Keyword(s):

Deep Learning ◽

Disease Diagnosis ◽

X Ray ◽

Beck Disease

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Covid-19 Diagnosis Model Using Deep Learning with Focal Loss Technique

2021 International Congress of Advanced Technology and Engineering (ICOTEN) ◽

10.1109/icoten52080.2021.9493477 ◽

2021 ◽

Author(s):

Ahmed Y. A. Saeed ◽

Abdulfattah E. Ba Alawi

Keyword(s):

Deep Learning ◽

Diagnosis Model

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Diabetes classification application with efficient missing and outliers data handling algorithms

Complex & Intelligent Systems ◽

10.1007/s40747-021-00349-2 ◽

2021 ◽

Author(s):

Hanaa Torkey ◽

Elhossiny Ibrahim ◽

EZZ El-Din Hemdan ◽

Ayman El-Sayed ◽

Marwa A. Shouman

Keyword(s):

Deep Learning ◽

Healthcare System ◽

Wearable Sensors ◽

Model Performance ◽

Analysis Model ◽

Human Errors ◽

Web Based ◽

Machine Learning Model ◽

Complete Dataset ◽

Smart Wearable

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

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