Lung disease detection using Self-Attention Generative Adversarial Capsule network optimized with sun flower Optimization Algorithm
Biomedical Signal Processing and Control
; 79, 2023.
Article
in English
| Scopus | ID: covidwho-2243008
ABSTRACT
Lung cancer is the uncontrolled growth of abnormal cells in one or both lungs. This is one of the dangerous diseases. A lot of feature extraction with classification methods were discussed previously regarding this disease, but none of the methods give sufficient results, not only that, those methods have high over fitting problem, as a result, the detection accuracy was minimizing. Therefore, to overcome these issues, a Lung Disease Detection using Self-Attention Generative Adversarial Capsule Network optimized with Sun flower Optimization Algorithm (SA-Caps GAN-SFOA-LDC) is proposed in this manuscript. Initially, NIH chest X-ray image dataset is gathered through Kaggle repository to diagnose the lung disease. Then, the chests X-ray images are pre-processed by using the contrast limited adaptive histogram equalization (CLAHE) filtering method to eliminate the noise and to enhance the image quality. These pre-processed outputs are fed to feature extraction process. In the feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Self-Attention based Generative Adversarial Capsule classifier for detecting the lung disease. The hyper parameters of SA-Caps GAN classifier is optimized using Sun flower Optimization Algorithm. The simulation is implemented in MATLAB. The proposed SA-Caps GAN-SFOA-LDC method attains higher accuracy 21.05%, 33.28%, 30.27%, 29.68%, 32.57% and 44.28%, Higher Precision 30.24%, 35.68%, 32.08%, 41.27%, 28.57% and 34.20%, Higher F-Score 32.05%, 31.05%, 36.24%, 30.27%, 37.59% and 22.05% analyzed with the existing methods, SVM-SMO-LDC, CNN-MOSHO-LDC, XGboost-PSO-LDC respectively. © 2022 Elsevier Ltd
Adaptive filtering; Biological organs; Classification (of information); Equalizers; Extraction; Feature extraction; Generative adversarial networks; Graphic methods; Image enhancement; MATLAB; Support vector machines; Adaptive histograms; Chest X-ray image; Contrast limited adaptive histogram equalization filtering scheme; Disease detection; Empirical wavelet transform; Filtering schemes; Histogram equalizations; Lung disease detection; Optimization algorithms; Self-attention generative adversarial capsule network; Sun flower optimization algorithm; Wavelets transform; Article; classifier; contrast limited adaptive histogram equalization; convolutional neural network; coronavirus disease 2019; deep belief network; diagnostic accuracy; histogram; human; image quality; information processing; lung disease; lung nodule; mammography; methodology; nonhuman; predictive value; self attention generative adversarial capsule network; sensitivity and specificity; signal processing; simulation; thorax radiography; wavelet transform; Wavelet transforms; Chests X-ray images
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Biomedical Signal Processing and Control
Year:
2023
Document Type:
Article
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