Development of Disease Diagnosis Model for CXR Images and Reports—A Deep Learning Approach

2021 ◽  
pp. 153-171
Author(s):  
Anandhavalli Muniasamy ◽  
Roheet Bhatnagar ◽  
Gauthaman Karunakaran
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Diagnosis Model

scholarly journals Deep Convolution Neural Networks for Medical Image Analysis

2018 ◽  
Vol 7 (3.33) ◽  
pp. 115 ◽  
Author(s):  
Myung Jae Lim ◽  
Da Eun Kim ◽  
Dong Kun Chung ◽  
Hoon Lim ◽  
Young Man Kwon
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Detection ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Histopathological Image ◽  
Approach Method

Breast cancer is a highly contagious disease that has killed many people all over the world. It can be fully recovered from early detection. To enable the early detection of the breast cancer, it is very important to classify accurately whether it is breast cancer or not. Recently, the deep learning approach method on the medical images such as these histopathologic images of the breast cancer is showing higher level of accuracy and efficiency compared to the conventional methods. In this paper, the breast cancer histopathological image that is difficult to be distinguished was analyzed visually. And among the deep learning algorithms, the CNN(Convolutional Neural Network) specialized for the image was used to perform comparative analysis on whether it is breast cancer or not. Among the CNN algorithms, VGG16 and InceptionV3 were used, and transfer learning was used for the effective application of these algorithms.The data used in this paper is breast cancer histopathological image dataset classifying the benign and malignant of BreakHis. In the 2-class classification task, InceptionV3 achieved 98% accuracy. It is expected that this deep learning approach method will support the development of disease diagnosis through medical images.  

A deep learning approach for patch-based disease diagnosis from microscopic images

2019 ◽  
pp. 109-127 ◽  
Author(s):  
Anson Simon ◽  
Ravi Vinayakumar ◽  
Viswanathan Sowmya ◽  
Kutti Padannayil Soman ◽  
Ennappadam Anathanarayanan A. Gopalakrishnan
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Microscopic Images

Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data

2021 ◽  
Vol 84 ◽  
pp. 168-177
Author(s):  
Ioannis D. Apostolopoulos ◽  
Dimitris I. Apostolopoulos ◽  
Trifon I. Spyridonidis ◽  
Nikolaos D. Papathanasiou ◽  
George S. Panayiotakis
Keyword(s):  
Cardiovascular Disease ◽  
Deep Learning ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Clinical Data ◽  
Perfusion Imaging ◽  
Disease Diagnosis ◽  
Learning Approach

scholarly journals Sensitivity and specificity evaluation of multiple neurodegenerative proteins for Creutzfeldt-Jakob disease diagnosis using a deep-learning approach

Prion ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 141-150
Author(s):  
Sol Moe Lee ◽  
Jae Wook Hyeon ◽  
Soo-Jin Kim ◽  
Heebal Kim ◽  
Ran Noh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Jakob Disease

scholarly journals A Fruit Tree Disease Diagnosis Model Based on Stacking Ensemble Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Honglei Li ◽  
Ying Jin ◽  
Jiliang Zhong ◽  
Ruixue Zhao
Keyword(s):  
Deep Learning ◽  
Ensemble Learning ◽  
Great Influence ◽  
Disease Diagnosis ◽  
Fruit Tree ◽  
Validation Dataset ◽  
Tree Diseases ◽  
Cost Constraints ◽  
Learning Classifiers ◽  
Diagnosis Model

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.

scholarly journals Optimal Deep Learning based Classification Model for Mitral Valve Diagnosis System

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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