Design ensemble deep learning model for pneumonia disease classification

Asnaoui, Khalid El

doi:10.1007/s13735-021-00204-7

Cited by 54 publications

(23 citation statements)

References 41 publications

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“…The objective of the optimizers is to keep updating the weights at every layer until the best learning of parameters in CNN (convolutional neural network) is realized. In the Stochastic Gradient Descent (SGD) method, the weights are updated for every single training set [ 7 ]. The formula for SGD optimizer is given in Eq.…”

Section: Methodsmentioning

confidence: 99%

“…Out of these models, DenseNet169 produced the best results among the mentioned experiments. El Asnaoui, K. [ 7 ] used an ensemble learning-based method using on fine-tuned versions of InceptionResNet_V2, ResNet50, and MobileNet_V2. It achieved an F1 score of 94.84% on the task of classifying chest X-rays images among bacterial, viral, COVID-19, and normal cases [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…El Asnaoui, K. [ 7 ] used an ensemble learning-based method using on fine-tuned versions of InceptionResNet_V2, ResNet50, and MobileNet_V2. It achieved an F1 score of 94.84% on the task of classifying chest X-rays images among bacterial, viral, COVID-19, and normal cases [ 7 ]. Acharya et al [ 18 ] used Siamese CNN (convolutional neural network) architecture to classify the images into three different classes such as bacterial pneumonia, viral pneumonia and normal cases.…”

Section: Introductionmentioning

confidence: 99%

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A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images

Trivedi

Gupta

2021

Multimed Tools Appl

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Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected by the disease and accurate manual detection is not easy. Generally, chest radiographs are used for the manual detection of pneumonia and expert radiologists are required for the assessment of the X-ray images. An automatic system would be beneficial for the diagnosis of pneumonia based on chest radiographs as manual detection is time-consuming and tedious. Therefore, a method is proposed in this paper for the fast and automatic detection of pneumonia. A deep learning-based architecture ‘MobileNet’ is proposed for the automatic detection of pneumonia based on the chest X-ray images. A benchmark dataset of 5856 chest X-ray images was taken for the training, testing, and evaluation of the proposed deep learning network. The proposed model was trained within 3 Hrs. and achieved a training accuracy of 97.34%, a validation accuracy of 87.5%, and a testing accuracy of 94.23% for automatic detection of pneumonia. However, the combined accuracy was achieved as 97.09% with 0.96 specificity, 0.97 precision, 0.98 recall, and 0.97 F-Score. The proposed method was found faster and computationally lesser expensive as compared to other methods in the literature and achieved a promising accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images

Trivedi

Gupta

2021

Multimed Tools Appl

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“…An ensemble classification system is a generic name for a system that combines multiple classification algorithms in order to enhance the prediction effectiveness of the system [47]. Several researchers have previously utilised classification ensembles and disputed their superiority to individual categorization [48][49][50][51][52][53]. The following diagram depicts the Ensemble Bootstrap aggregating learning algorithm.…”

Section: Ensemble Bootstrap Aggregating Learning Algorithmmentioning

confidence: 99%

Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model

Osman¹

2022

Computers, Materials &Amp; Continua

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This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical applications. The proposed model is based on an Ensemble boosting Neural Network architecture and can automatically detect discriminatory features on chest X-ray images through Two Step-As clustering algorithm with rich filter families, abstraction and weight-sharing properties. In contrast to the generally used transformational learning approach, the proposed model was trained before and after clustering. The compilation procedure divides the datasets samples and categories into numerous sub-samples and subcategories and then assigns new group labels to each new group, with each subject group displayed as a distinct category. The retrieved characteristics discriminant cases were used to feed the Multiple Neural Network method, which was then utilised to classify the instances. The Two Step-AS clustering method has been modified by pre-aggregating the dataset before applying Multiple Neural Network algorithm to detect COVID-19 cases from chest X-ray findings. Models for Multiple Neural Network and Two Step-As clustering algorithms were optimised by utilising Ensemble Bootstrap Aggregating algorithm to reduce the number of hyper parameters they include. The tests were carried out using the COVID-19 public radiology database, and a cross-validation method ensured accuracy. The proposed classifier with an accuracy of 98.02% percent was found to provide the most efficient outcomes possible. The result is a lowcost, quick and reliable intelligence tool for detecting COVID-19 infection.

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“…Classification is a popular technique in the field of biomedical condition monitoring and detection purposes. In few recent studies, the classification technique is used for brain disease [18,24] and tumour detection from medical image data [23,36], analysis of chest disease [19], electromyogram (EMG) signal classification [17], pneumonia disease classification [14], detection of epileptic seizure from EEG signals [21], arrhythmia detection [25], even detection of Covid-19 from x-ray [3] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

Sarkar

Goswami

Ghosh

2021

Preprint

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Hypertension or high blood pressure is a severe health issue in the modern world, especially in this pandemic scenario, that can cause many heart related diseases or even death, and it is increasing day by day. For this reason, a reliable, automatic and easy to use system for hypertensive subject detection is an important focus for the researchers. Biopotential signals can play a pivotal role in this regard. Though, few strategies were proposed based on electrocardiogram (ECG) or electrodermal (EDA) signals, but those require special circuitry, as well as trained persons. In this article, a method is proposed to classify hypertensive and normotensive subjects using differential biopotential signals. Neither special circuitry, nor much expertise is required for handling this system. It was assumed that progression of rest is dependent upon blood pressure. To serve the purpose, signals were acquired from both hypertensive and normotensive subjects bilaterally for 10 continuous minutes. Result of the random forest (RF) classification establishes that from the analysis of the progression of the bilaterally acquired differential biopotential signals, hypertensive subjects can be distinguished from normotensive subjects.

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Design ensemble deep learning model for pneumonia disease classification

Cited by 54 publications

References 41 publications

A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images

A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images

Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model

Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

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