Lung Diseases Classification by Analysis of Lung Tissue Densities

Alves, Shara S. A.; Rebouças, Elizângela de S.; Oliveira, Saulo A. F.; Braga, Alan M.; Filho, Pedro Pedrosa Rebouças

doi:10.1109/tla.2020.9381790

Cited by 4 publications

(4 citation statements)

References 41 publications

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“…DenseNet-121, ResNet-18, and GoogLeNet CNN models were employed. Aside from the approaches described above, we examined several other publications on lung disease categorization in [6,8,13,21,49,62,65,69] using various methodologies.…”

Section: Automatic State-of-art Methodsmentioning

confidence: 99%

“…Most of the techniques employed the transfer learning mechanism using the pre-trained models. All the above methods [ 1 , 2 , 5 – 8 , 13 , 20 – 22 , 27 , 28 , 31 , 32 , 45 , 47 , 49 , 56 – 58 , 62 , 65 , 67 , 69 , 73 – 75 , 77 , 79 , 83 ] were focused on automatic lung disease classification using chest X-ray images. Despite promising outcomes of such CAD systems, some challenges are still unaddressed for the chest X-ray image-based lung disease classification.…”

Section: Related Workmentioning

confidence: 99%

“…Despite promising outcomes of such CAD systems, some challenges are still unaddressed for the chest X-ray image-based lung disease classification. We summarize these challenges as: None of the state-of-art methods [ 1 , 2 , 5 – 8 , 13 , 20 – 22 , 27 , 28 , 31 , 32 , 45 , 47 , 49 , 56 – 58 , 62 , 65 , 67 , 69 , 73 – 75 , 77 , 79 , 83 ] focused on image quality enhancement. It limits the reliability of the proposed models as the low-quality X-ray images failed to produce the vital ROI-specific features using the CNN models.…”

Section: Related Workmentioning

confidence: 99%

“…None of the state-of-art methods [ 1 , 2 , 5 – 8 , 13 , 20 – 22 , 27 , 28 , 31 , 32 , 45 , 47 , 49 , 56 – 58 , 62 , 65 , 67 , 69 , 73 – 75 , 77 , 79 , 83 ] focused on image quality enhancement. It limits the reliability of the proposed models as the low-quality X-ray images failed to produce the vital ROI-specific features using the CNN models.…”

Section: Related Workmentioning

confidence: 99%

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

Farhan

Yang

2023

Multimed Tools Appl

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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.

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