Abstract
Pneumonia is an infectious disease that attacks the lungs, causing the air sacs in the lungs to become inflamed and swollen. Pneumonia is caused by fungi, bacteria, and viruses. Pneumonia can affect anyone, including children. The most successful type of method for analyzing images to date is the Convolutional Neural Network (CNN). The Convolutional Neural Network classification is implemented based on multiple extraction features. The purpose of this study is to evaluate the performance of ID-Convolutional to classify pneumonia with various CNN architectures to produce the best performance in accuracy and compare it with the other baseline methods that have been made. Experiments are carried out based on the number of hidden layers and configuration parameters. The parameters used are the epoch, kernel, strides, and pool size. The result shows that the proposed method achieves 94% inaccuracy and 0.93 in AUC. Besides, CNN has a competitive result compare to the other baseline methods.
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Fathurahman, M., Fauzi, S.C., Haryanti, S.C., Rahmawati, U.A., Suherlan, E. (2020). Implementation of 1D-Convolution Neural Network for Pneumonia Classification Based Chest X-Ray Image. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_18
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DOI: https://doi.org/10.1007/978-3-030-36056-6_18
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