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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Madalu Palakshamurthy, Pavan Kumar
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Computer Science & IT Engineering

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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.
Chest X-ray image classification using deep belief network with Al-Biruni earth radius and particle swarm optimization Selva Raj, Rakesh; Madalu Palakshamurthy, Pavan Kumar; Eswarappa Rangaswamy, Bidarakere
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5120-5130

Abstract

Chest X-ray (CXR) is a widely employed radiological clinical assessment tool that provides a quick and effective means of classifying various diseases using CXR images. However, several researchers face challenges with CXR images due to imbalanced datasets and image quality issues. Pre-processing is performed using contrast limited adaptive histogram equalization (CLAHE) to enhance image quality and mitigate noise in the data. The synthetic minority oversampling technique (SMOTE) is applied to create synthetic samples for the minority class and handle class imbalance. The MobileNetV2 performs depth-wise separable convolution is used for feature extraction, while maintaining high efficiency for CXR images. This research proposes a deep belief network (DBN) to classify CXR, which helps capture hierarchical features and complex patterns in CXR images. The combination of particle swarm optimization (PSO) and Al-Biruni earth radius (BER) method is employed for hyperparameter tuning with enhanced DBN classification accuracy. Furthermore, BER is integrated with the PSO algorithm to balance exploration and exploitation while the fitness function is fine-tuned for optimal DBN classification performance. The proposed PSOBER-DBN achieves a high accuracy of 99.86% on the CXR14 dataset, in comparison to existing techniques such as the multi-level residual feature fusion network (MLRFNet).
Co-Authors Eswarappa Rangaswamy, Bidarakere Raj, Rakesh Selva Rangaswamy, Bidarakere Eswarappa Selva Raj, Rakesh