Pneumonia Detection Using CNN based Feature Extraction

By Dimpy Varshni, Kartik Thakral, Lucky Agarwal, Rahul Nijhawan, Ankush Mittal. IEEE International Conference on Electrical, Computer and Communication Technologies, 2019

337 citations recorded by the source corpus.

Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2e9bf21eaedf7a18e099d1e0d8faba07d36fc154

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A scoping review of transfer learning research on medical image analysis using ImageNet (2020, background): “analyzed lung X-rays [128,130,131], suggesting that deeper CNN models are optimal for this problem, which is”
Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning (2020, methodology): “[46] proposed pre-trained ConvNet models (VGG16, Xception, Res50, Dense-121, and Dense-169) as feature-extractors followed by different classifiers (SVM, Random Forest, k-nearest neighbors, Naïve Bayes) for the detection of normal and abnormal pneumonia X-Rays images.”
COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images (2020, neutral): “In [22] , the authors propose a CNN model for pneumonia detection.”