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Enhanced U-Net-based segmentation and heuristically improved deep neural network for pulmonary emphysema diagnosis

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Abstract

Pulmonary emphysema is a main part of chronic obstructive pulmonary disease and lung cancer. Though, quantitative emphysema severity prediction is essential in patients with unclear lung cancer categories. Thus, diagnosis of pulmonary emphysema at the early stage is more significant and could save human life. Moreover, non-invasive positive pressure ventilation is a life-saving method that focuses on reducing the complexities in patients. When failure occurs in non-invasive positive pressure ventilation, there are more chances for mortality, which shows the significance of rational diagnosis. The delay in endotracheal intubation is avoided by developing different approaches, which leads to more demand. This paper plans to develop an enhanced system for pulmonary emphysema diagnosis using deep learning-based segmentation and classification. Initially, the detection model considers pre-processing with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filtering. Moreover, lung segmentation is the major part of pulmonary emphysema diagnosis. Here, the enhanced U-Net model is utilized for lung segmentation by considering the multi-objective function with the developed algorithm. Then, the feature extraction is done using the local tri-directional weber pattern and local directional pattern descriptors. The extracted features are classified by the heuristically improved deep neural network based on a new algorithm, which will optimally diagnose the severity of pulmonary emphysema. Both segmentation and classification will be enhanced by proposing a new Electric Fish-based Grey Wolf Optimization (EF-GWO). Here, various performance metrics, like accuracy, precision, specificity, etc., on the public dataset by comparing with other models. The accuracy of the EF-GWO with E-UNet is 96%. The accuracy of the suggested developed EF-GWO based on HI-DNN is 97%. Hence, it verifies the superior performance of the recommended pulmonary emphysema disease diagnosis method with improved segmentation and classification techniques compared to other existing methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, 602105, India

    K Ananthajothi

  2. Department of Data Science and Business Systems, S.R.M Institute of Science and Technology, Kattankulathur, 603203, India

    P Rajasekar

  3. Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602117, India

    M Amanullah

Authors
  1. K Ananthajothi
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  2. P Rajasekar
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  3. M Amanullah
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Corresponding author

Correspondence to K Ananthajothi.

Abbreviations

Abbreviations

BSO:

Beetle Swarm Optimization

BDP:

Balanced Probability Distribution

CLAHE:

Contrast Limited Adaptive Histogram Equalization

EF-GWO:

Electric Fish-based Grey Wolf Optimization

CT:

Computed Tomography

E-UNet:

Enhanced U-Net

DNN:

Deep Neural Network

COPD:

Chronic Obstructive Pulmonary Disease

MCC:

Mathews Correlation Coefficient

HI-DNN:

Heuristically Improved Deep Neural Network

ReLU:

Rectified Linear Unit

NIPPV:

Non-Invasive Positive Pressure Ventilation

SVM:

Support Vector Machine

GWO:

Grey Wolf Optimization

SPECT:

Single-Photon Emission Computed Tomography

FDR:

False Discovery Rate

LTriWP:

Local Tri-directional Weber Pattern

ISP:

IntelliSpace Portal

NPV:

Negative Predictive Value

CAD:

Computer-Aided Diagnosis

DT:

Decision Trees

EFO:

Electric Fish Optimization

FNR:

False Negative Rate

LDP:

Local Directional Pattern

NN:

Neural Networks

PSO:

Particle Swarm Optimization

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Ananthajothi, K., Rajasekar, P. & Amanullah, M. Enhanced U-Net-based segmentation and heuristically improved deep neural network for pulmonary emphysema diagnosis. Sādhanā 48, 33 (2023). https://doi.org/10.1007/s12046-023-02092-5

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  • DOI: https://doi.org/10.1007/s12046-023-02092-5

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