Pneumonia Detection Using X-ray Images and Deep Learning

Khamkar, Chinmay; Shah, Mansi; Kalyani, S. Nithya; Bhowmick, Kiran

doi:10.1007/978-981-16-0882-7_10

Cited by 2 publications

(2 citation statements)

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“…Deep learning‐based methods, 26–29 on the other hand, do not require handcrafted features to be extracted from the images for classification, instead the models learn the relevant informative features automatically. CNN‐based models are preferred for image classification problems since they efficiently extract translationally invariant features from the images using the convolution of the input image and the filters.…”

Section: Related Workmentioning

confidence: 99%

“…The results obtained from these methods justify that they are not suitable for practical applications, and also, being evaluated on such small data sets make them difficult to generalize. Deep learning-based methods, [26][27][28][29] on the other hand, do not require handcrafted features to be extracted from the images for classification, instead the models learn the relevant informative features automatically. CNN-based models are preferred for image classification problems since they efficiently extract translationally invariant features from the images using the convolution of the input image and the filters.…”

Section: Related Workmentioning

confidence: 99%

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Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

Chattopadhyay

Kundu

Singh

et al. 2021

Int J of Intelligent Sys

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Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X-ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X-ray images, a more robust method is therefore required, which can be achieved by using a computer-aided diagnosis (CAD) system. In this study, we develop a two-stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and timeefficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet-201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing-based local search algorithm. The optimized feature subset is utilized for the classification of "Pneumonia" and "Normal" X-ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the

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Section: Related Workmentioning

confidence: 99%