Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods

Li, Li; Alimu, Ayiguli; Luan, Qiyun; Yang, Bo‐Yi; Yilamujiang, Subinuer; Gong, Haimei; Zulipikaer, Abudureherman; Xu, Jin; Zhong, Xuemei; Ren, Jiangtao; Zou, Xiaoguang

doi:10.3389/fpubh.2022.881234

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…Various models were developed using supervised, semi-supervised and unsupervised methods, as summarized in Tables 1, 2, and 3. Automated deep learning techniques play a significant role in accurately predicting lung diseases (Wang et al 2022b;Li et al 2022;Walsh et al 2022;Mohamed 2022). Predicting progressive fibrotic lung disease enhances outcomes with deep learning-based usual interstitial pneumonia probability on High-Resolution CT (Walsh et al 2022).…”

Section: Predicting Lung Diseases Via DL Modelsmentioning

confidence: 99%

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Vohra,

Mittal

2023

J Exp Bio & Ag Sci

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Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy.

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Section: Predicting Lung Diseases Via DL Modelsmentioning

confidence: 99%

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Vohra,

Mittal

2023

J Exp Bio & Ag Sci

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“…To transform text into a data structure that a computer can process, the text needs to be sliced into semantic units. In the first step of our machine learning, we used the jieba module (the Chinese word segmentation module in the Python) to segment the names of Chinese firms 29,30 . The Chinese Thesaurus was used to perform forward maximum matching for POI name field information, which was segmented into several words for keyword recognition, manual tagging, or machine learning training.…”

Section: Map Poi Name-manufacturing Type Classification Algorithm Bas...mentioning

confidence: 99%

China’s Gridded Manufacturing Dataset

et al. 2022

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The growth of the manufacturing industry is the engine of rapid economic growth in developing regions. Characterizing the geographical distribution of manufacturing firms is critically important for scientists and policymakers. However, data on the manufacturing industry used in previous studies either have a low spatial resolution (or fuzzy classification) or high-resolution information is lacking. Here, we propose a map point-of-interest classification method based on machine learning technology and build a dataset of the distribution of Chinese manufacturing firms called the Gridded Manufacturing Dataset. This dataset includes the number and type of manufacturing firms at a 0.01° latitude by 0.01° longitude scale. It includes all manufacturing firms (classified into seven categories) in China in 2015 (4.56 million) and 2019 (6.19 million). This dataset can be used to characterize temporal and spatial patterns in the distribution of manufacturing firms as well as reveal the mechanisms underlying the development of the manufacturing industry and changes in regional economic policies.

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“…Comparative methods: K-Nearest Neighbor (KNN) Classifier (B 1 )[32], Support Vector Machine (SVM) Classifier (B 2 )[33],Decision Tree (DT) Classifier (B 3 )[34], Neural Network (B 4 )[35], bi-directional long short term memory (Bi-LSTM) (B 5 )[36], 3D Deep-CNN (B 6 ) [37], 3D Deep-CNN With Jelly fish optimization (JFO) (B 7 ) [38], 3D Deep-CNN With electric fish optimization (EFO) (B 8 ) [39] is compared with JEO-3D deep CNN. Comparative analysis with TP for dataset 1: In Figure 7, a comparative analysis of performance parameters for breast cancer detection in Dataset 1 is presented.…”

mentioning

confidence: 99%

An Efficient Breast Cancer Detection Using Jelly Electrophorus Optimization Based Deep 3D Convolution Neural Networks (CNN)

Patel

2023

CTOIJ

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Breast cancer is one of the world’s most serious diseases that affect millions of women every year, and the number of people affected is increasing. The only practical way to lessen the impact of a disease is through early detection. Researchers have developed a variety of methods for identifying breast cancer, and using histopathology images as a tool has been quite successful. As an enhancement, this research develops a jelly electrophorus optimization-based 3D density connected deep Convolution Neural Networks (CNN) for the identification of breast cancer using histopathology images and input is collected and pre-processing is performed for improve the histopathology image’s properties. Then the feature extraction is performed through VGG-16, AlexNet, ResNet-101, statistical features. Finally, these concatenated features are fed forwarded to 3D density connected 3D deep Convolution Neural Networks (CNN) classifier it automatically detects breast cancer effectively with the help of jelly electrophorus optimization. The proposed jelly electrophorus optimization adjusts the classifier’s weights and bias. In terms of accuracy, sensitivity, and specificity, the proposed JEO-3D deep Convolution Neural Networks (CNN) approach attains values of 93.73%, 95.31%, and 94.14% for dataset 1 and 88.75%, 90.32%, and 89.15% for dataset 2, which is more successful.

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Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods

Cited by 12 publications

References 33 publications

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

China’s Gridded Manufacturing Dataset

An Efficient Breast Cancer Detection Using Jelly Electrophorus Optimization Based Deep 3D Convolution Neural Networks (CNN)

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