Pneumonia Detection and Classification Using Chest X-Ray Images with Convolutional Neural Network

Angeline, R.; Mrithika, Munukoti; Raman, Atmaja; Warrier, Prathibha

doi:10.1007/978-3-030-41862-5_69

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…During the learning stage, they used the exchange learning and modifying technique. Method for detecting pneumonia using X-ray images and CNNs had proposed in [5]. They programmed the CNN to classify the information in the X-ray image as ordinary or pneumonic.…”

Section: Automatic State-of-art Methodsmentioning

confidence: 99%

“…Many investigations uncovered that X-rays have a practical strategy for sickness diagnosis while uncovering the obsessive changes alongside their monetary effectiveness and non-obtrusive properties [80]. The lung diseases can be addressed in chest X-ray images in the type of solidifications, dulled costophrenic points, extensively disseminated knobs, cavitations, and invades [4]. In the investigation of the X-ray image of the patient, radiologists recognize a few conditions like pneumonia, nodule, pleurisy, radiation, invasion, fractures, pneumothorax, pericarditis, and so on [54,81].…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm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

Section: Introductionmentioning

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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“…They used the transfer learning and fine-tuning approach during the learning phase. The approach for detecting pneumonia is proposed in Angeline et al ( 2020 ) using X-ray images and CNN. They trained the CNN to classify the input X-ray image into normal or pneumonic class.…”

Section: Related Workmentioning

confidence: 99%

“…Many studies revealed that X-ray technique is a cost-effective method for disease diagnosis while providing the pathological alterations along with its economic efficiency and non-invasive properties (Yasin et al 2020 ). The lung infections in chest X-ray images have been found in the form of consolidations, blunted costophrenic angles, broadly distributed nodules, cavitations, and infiltrates (Angeline et al 2020 ). Therefore, radiologists detect several conditions like pneumonia, pleurisy, nodule, effusion, infiltration, fractures, pneumothorax, pericarditis using X-ray images (Padma and Kumari 2020 ; Rousan et al 2020 ).…”

Section: Introductionmentioning

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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“…Many studies have shown that the X-ray technique is a cost-effective tool for illness detection, giving pathological changes as well as economic productivity and non-invasive qualities. Lung infections have been observed in chest X-ray images as blunted costophrenic angles, consolidations, cavitation, infiltrates and widely dispersed nodules [ 12 ]. As a result, radiologists use X-ray images to diagnose illnesses such as pneumonia, infiltration, nodule, fractures, pleurisy, pneumothorax, pericarditis and effusion [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

et al. 2022

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According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model’s ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.

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Pneumonia Detection and Classification Using Chest X-Ray Images with Convolutional Neural Network

Cited by 7 publications

References 7 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

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

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

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