Pneumonia Image Classification Using CNN with Max Pooling and
Average Pooling

Pneumonia Image Classification Using CNN with Max Pooling and
Average Pooling

pneumonia, image classification, Convolutional Neural Network

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

Annisa Fitria Nurjannah,
2Andi Shafira Dyah Kurniasari, 3Zamah Sari, 4Yufis Azhar

Informatics, Faculty of Engineering, University of Muhammadiyah Malang

29-04-2022

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Indonesia

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