Pneumonia Classification Based on Lung CT Scans Using Vgg-19
DOI:
10.33395/sinkron.v8i4.12778Keywords:
Pneumonia Detection; CT Scan Classification; VGG-19 Model; Deep SMOTE; Data Augmentation.Abstract
This research harnesses technology for critical health applications, specifically, pneumonia detection through medical imaging. X-ray photography allows radiologists to visualize the patient's health state, including the detection of lung infections signifying pneumonia. The study's centerpiece is the application of the VGG-19 model in classifying lung CT scan images, helping discern normal from pneumonia-indicative conditions. A comprehensive preprocessing procedure is employed, entailing pixel rescaling and data augmentation techniques. To address data imbalance, a critical issue in machine learning, we incorporate the Synthetic Minority Over-sampling Technique (SMOTE). The developed VGG-19 model demonstrates impressive performance, achieving a 94.6% accuracy rate in classifying lung CT scans. This finding underscores the potential of the VGG-19 model as a reliable tool for pneumonia detection based on lung CT scans. Such a tool could revolutionize the field, providing an efficient and accurate method for early pneumonia diagnosis, thereby allowing for timely treatment.
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Copyright (c) 2023 Adya Zizwan Putra, Dani Situmorang, Gilang wahyudi, Jaya giawa, Rico tarigan
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