Pneumonia Image Classification Using CNN with Max Pooling and Average Pooling

• Published in: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (Online) Vol. 6; no. 2; pp. 330 - 338
• Main Authors: Annisa Fitria Nurjannah, Andi Shafira Dyah Kurniasari, Zamah Sari, Yufis Azhar
• Format: Journal Article
• Language: English
• Published: Ikatan Ahli Informatika Indonesia 29-04-2022
• Subjects: convolutional neural network; image classification; pneumonia
• ISSN: 2580-0760; 2580-0760
• DOI: 10.29207/resti.v6i2.4001

Pneumonia is still a frequent cause of death in hundreds of thousands of children in most developing countries and generally detected clinically through chest radiographs. This method still difficult to detect the disease and requires a long time to produce a diagnosis. To simplify and shorten the detection process, we need a faster method and more precise in diagnosing pneumonia. This study aims to classify chest x-ray images using the CNN method to diagnose pneumonia. The proposed CNN model will be tested using max & average pooling. The proposed model is a development of the model in previous studies by adding batch normalization, dropout layer, and the number of epochs used. To maximize model performance, the dataset used will be optimized with oversampling & data augmentation techniques. The dataset used in this study is "Chest X-Ray Images (Pneumonia)" with a total of 5,856 data divided into two classes, namely Normal and Pneumonia. The proposed model gets 98% results using average pooling where the results increase by 9-13% better than the previous study. This is because overall pixel value of the image is highly considered to classify normal lungs and pneumonia.