Classification of Abnormality in Chest X-Ray Images by Transfer Learning of CheXNet

Almuhayar, Mawanda (2019) Classification of Abnormality in Chest X-Ray Images by Transfer Learning of CheXNet. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Deep learning nowadays has attracted attention, especially in medical images classification because of its effectiveness and good performance that can compete with the medical images expert. Despite these successes there are the strong belief among experts that deep learning only
efficient for the big datasets and for small datasets deep learning would produce a bad performance. For this study, it is aimed to build a deep learning model for image classification that can achieve high accuracy using chest x-ray images with relatively small dataset. We classify all normal chest x-ray images and all abnormalities in chest x-ray images into a binary classifier. We built and tested our model using the public dataset of Shenzen Hospital dataset.
We use different type of input images based on different preprocessing and different type of learning technique so that the model can perform accurate classification for this particular dataset. Based on the result, pre-trained CheXNet with new trained fully connected network on cropped dataset can achieve the accuracy 0.8761, the sensitivity 0.8909, and the specificity 0.8621. The performance of the model also influenced by the certain area in the images, like other region outside the lung and black region outside the body.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	chest x-ray, deep learning, transfer learning, classification, abnormalities, preprocessing, CheXNet
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science)
Divisions:	Faculty of Mathematics, Computation, and Data Science > Statistics > 49101-(S2) Master Thesis
Depositing User:	Almuhayar Mawanda
Date Deposited:	23 Jul 2026 04:26
Last Modified:	23 Jul 2026 04:26
URI:	http://repository.its.ac.id/id/eprint/68415

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