RETRACTED ARTICLE: Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm

Abstract

The chest X-ray images provide vital information about the congestion cost-effectively. We propose a novel Hybrid Deep Learning Algorithm (HDLA) framework for automatic lung disease classification from chest X-ray images. The model consists of steps including pre-processing of chest X-ray images, automatic feature extraction, and detection. In a pre-processing step, our goal is to improve the quality of raw chest X-ray images using the combination of optimal filtering without data loss. The robust Convolutional Neural Network (CNN) is proposed using the pre-trained model for automatic lung feature extraction. We employed the 2D CNN model for the optimum feature extraction in minimum time and space requirements. The proposed 2D CNN model ensures robust feature learning with highly efficient 1D feature estimation from the input pre-processed image. As the extracted 1D features have suffered from significant scale variations, we optimized them using min-max scaling. We classify the CNN features using the different machine learning classifiers such as AdaBoost, Support Vector Machine (SVM), Random Forest (RM), Backpropagation Neural Network (BNN), and Deep Neural Network (DNN). The experimental results claim that the proposed model improves the overall accuracy by 3.1% and reduces the computational complexity by 16.91% compared to state-of-the-art methods.

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features

“…The primary limitations include the lack of image pre-processing, the absence of information on optimal features, and low accuracy in multi-class classification. Meanwhile, Farhan and his colleagues [14] looked into how to diagnose COVID-19 pneumonia using the COVID-19 Radiography Database (C19RD), which contains 2905 images, and the Chest X-ray Images for Pneumonia (CXIP) dataset, consisting of 5856 images. In particular, they used the ResNet50 feature extractor and the Hybrid Deep Learning Algorithm (HDLA-DNN) to differentiate between disease classes (e.g., non-COVID-19 pneumonia or COVID-19 pneumonia) and healthy classes.…”

“…Addressing these challenges and enhancing clinical diagnosis, we employed an automated system for chest radiographic images, which can efficiently diagnose respiratory issues. Numerous recently developed automated systems were examined in our literature review [5,[9][10][11][12][13][14][15][16][17][18][19][20][21]. Challenges were encountered by those systems in data handling during image processing and optimal feature extraction, complicated quantification, and high runtime complexity issues in classifying chest X-ray images.…”

Investigation of Different Pre-processing Quality Enhancement Techniques on X-ray Images

“…Related work: Farhan. [6] Proposed novel framework HDLA (Hybrid deep learning Algorithm) for classification of chest Xray. Author improved quality of chest X-ray using different filtering techniques with minimum data loss.…”

Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images

“…D3SENET (13) , a hybrid feature extraction network combining multiple architectures and employing traditional machine learning methods for classification, including SVMs. Farhan et al (14) proposed a hybrid network for lung diseases classification, in which 2D CNN network was designed to extract features from X-ray images, and the features were optimized using min-max scaling and classified using various ML algorithms.…”