Using Various Convolutional Neural Network to Detect Pneumonia from Chest X-Ray Images: A Systematic Literature Review

International Journal on Informatics Visualization: JOIV

article

Darnell Kikoo¹  Bryan Tamin¹  Stephen Hardjadilaga¹  - Anderies¹  Irene Anindaputri Iswanto¹ 

[1] Bina Nusantara University Jakarta

关键词: Pneumonia;    lung diseases;    X-rays;    CXR;    radiography;    deep learning;    convolutional neural network;    CNN;    classification;   

DOI  :  10.30630/joiv.7.2.1015

来源: Politeknik Negeri Padang

【 摘 要 】

Pneumonia is one of the world's top causes of mortality, especially for children. Chest X-rays serve an important part in diagnosing pneumonia due to the cost-effectiveness and quick advancement of the technology. Detecting Pneumonia through Chest X-rays (CXR) is a challenging and time-consuming process requiring trained professionals. This issue has been solved by the development of automation technology which is machine learning. Moreover, Deep Learning (DL), a machine learning specification that uses an algorithm that resembles the human brain, can predict more accurately and is now dependable enough to predict pneumonia. As time passes, another Deep Learning improvement has been made to produce a new method called Transfer Learning, that is done by extracting specific layers from some pre-trained network to be used on other datasets, which reduces the training time and improves the model performance. Although numerous algorithms are already available for pneumonia identification, a comprehensive literature evaluation and clinical recommendations are still small in numbers. This research will assist practitioners in choosing some of the best procedures from the recent research, reviewing the available datasets, and comprehending the outcomes gained in this domain. The reviewed papers show that the best score for predicting pneumonia using DL from CXR was 99.4% accuracy. The exceptional techniques and results from the reviewed papers served as great references for future research.

【 授权许可】

Unknown