Citation

Toh, Jun Jie and Sayeed, Md Shohel (2024) Review on Detecting Pneumonia in Deep Learning. International Journal on Robotics, Automation and Sciences, 6 (1). pp. 70-77. ISSN 2682-860X

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Abstract

Deep learning is a machine learning technique that has been optimized for image classification and object detection. Deep learning has brought huge advancement to the medical field as it helps to diagnose various diseases through computed tomography (CT) scan or X-ray images. Pneumonia is a respiratory disease, and it is one of the killer diseases that causes numerous death all around the world. In 2019, the outbreak of COVID-19 has increased the number of pneumonia patients tremendously. With the increasing number of patients, the clinical and medical facilities have become insufficient. The lack of doctors and radiologists to diagnose pneumonia has caused a high number of patients to be misdiagnosed. Chest image is one of the most effective methods to diagnose this disease, however, examining the X-ray or CT images requires specialists such as radiologists. Meanwhile, examining chest CT or X-ray images might be subjective as the presence of pneumonia can be unclear in the images. The main objective of this paper is to provide a comprehensive review of recent advancement in the diagnosis of pneumonia with deep learning, including state-of-art methodology, datasets, discussion, challenges, and future improvements.

Item Type:	Article
Uncontrolled Keywords:	Deep Learning, COVID-19, Convolutional Neural Network, Pneumonia Diagnosis, Chest X-Ray, Chest CT Scan
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	06 Sep 2024 07:22
Last Modified:	06 Sep 2024 07:22
URII:	http://shdl.mmu.edu.my/id/eprint/12970

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