Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Raj, Rakesh Selva
Unknown Affiliation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Multi-class chronic lung disease classification based on guided backpropagation convolutional neural network using chest X-ray images Raj, Rakesh Selva; Madalu Palakshamurthy, Pavan Kumar; Rangaswamy, Bidarakere Eswarappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1328-1338

Abstract

Clinical diagnosis is crucial as chronic lung disease is a leading cause of mortality worldwide. Chest X-ray imaging is essential for the early and accurate diagnosis of lung diseases. However, due to the complexity of pathological abnormalities and detailed annotation, the computer-aided diagnosis of lung diseases is challenging. To overcome this challenge, this research proposes the guided backpropagation convolutional neural network (GBPCNN) for the classification of chronic lung disease into 14 classes, by adjusting the network’s weights in CNN layers. The GBP technique enhances result accuracy by pinpointing the regions in an input image. Initially, the chest X-ray radiography (CXR) dataset is collected for estimating the effectiveness of the classifier. After collecting the dataset, the pre-processing is performed by utilizing image denoising using gaussian filter and normalization techniques. Then, the pre-processed data is fed to the feature extraction process, and it is done by using EfficientNetB2. Finally, extracted features are provided to the classification process to categorize chronic lung disease into 14 classes. The experimental results show that the proposed GBPCNN method attains better results and it achieves the accuracy of 97.94% as compared to the existing approaches like MobileLungNetV2 and CX-Ultranet. These results highlight the potential of our approach for clinical applications.
Co-Authors Madalu Palakshamurthy, Pavan Kumar Rangaswamy, Bidarakere Eswarappa