Automatic Detection of Pneumonia from Chest X-Rays Using Deep Learning

Nath, Mriganka; Choudhury, Chandrajit

doi:10.1007/978-981-15-6315-7_14

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…They build deep leftover learning using different convolutional networks for grouping. Another method for locating pneumonia infections based on CNN had given in [56]. They prepared X-ray images of common and unusual illnesses and built a model to detect the presence of pneumonia.…”

Section: Automatic State-of-art Methodsmentioning

confidence: 99%

RETRACTED ARTICLE: Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm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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Section: Automatic State-of-art Methodsmentioning

confidence: 99%

RETRACTED ARTICLE: Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm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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“…To resolve this challenge, various studies have been done on chest X-rays. An innovative method was introduced utilizing the well-known VGG16 model [20], enhanced by fine-tuning its deep layers. This approach was not confined to merely identifying the presence of pneumonia but also extended to gauge its severity-a crucial aspect for clinical decision-making.…”

Section: Related Workmentioning

confidence: 99%

“…The existing studies in the literature review offer a breadth of approaches to pneumonia classification, each with distinct advantages and shortcomings. Many of these methods, such as VGG16 and its variations [20][21][22]31] although accurate, suffer from high computational costs and complexity, which can be prohibitive in clinical settings, especially in developing countries. Others, like the QCSA network [27], introduce additional complexity through attention mechanisms, while methods involving adversarial training or large ensembles of CNNs may not be feasible due to the need for substantial computational resources and potential overfitting issues.…”

Section: Related Workmentioning

confidence: 99%

Efficient Deep Learning Approach for the Classification of Pneumonia in Infants from Chest X-Ray Images

AlGhamdi

2024

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“…For classification, they build deep residual learning with the separable convolutional networks. Another CNN-based pneumonia disease detection system was introduced in Nath and Choudhury ( 2020 ). They trained the X-ray images of normal and abnormal conditions and prepared a model to detect the presence of pneumonia.…”

Section: Related Workmentioning

confidence: 99%

Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques

Goyal

Singh

2021

J Ambient Intell Human Comput

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Since the arrival of the novel Covid-19, several types of researches have been initiated for its accurate prediction across the world. The earlier lung disease pneumonia is closely related to Covid-19, as several patients died due to high chest congestion (pneumonic condition). It is challenging to differentiate Covid-19 and pneumonia lung diseases for medical experts. The chest X-ray imaging is the most reliable method for lung disease prediction. In this paper, we propose a novel framework for the lung disease predictions like pneumonia and Covid-19 from the chest X-ray images of patients. The framework consists of dataset acquisition, image quality enhancement, adaptive and accurate region of interest (ROI) estimation, features extraction, and disease anticipation. In dataset acquisition, we have used two publically available chest X-ray image datasets. As the image quality degraded while taking X-ray, we have applied the image quality enhancement using median filtering followed by histogram equalization. For accurate ROI extraction of chest regions, we have designed a modified region growing technique that consists of dynamic region selection based on pixel intensity values and morphological operations. For accurate detection of diseases, robust set of features plays a vital role. We have extracted visual, shape, texture, and intensity features from each ROI image followed by normalization. For normalization, we formulated a robust technique to enhance the detection and classification results. Soft computing methods such as artificial neural network (ANN), support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier, and deep learning classifier are used for classification. For accurate detection of lung disease, deep learning architecture has been proposed using recurrent neural network (RNN) with long short-term memory (LSTM). Experimental results show the robustness and efficiency of the proposed model in comparison to the existing state-of-the-art methods.

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Automatic Detection of Pneumonia from Chest X-Rays Using Deep Learning

Cited by 8 publications

References 4 publications

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

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

Efficient Deep Learning Approach for the Classification of Pneumonia in Infants from Chest X-Ray Images

Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques

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