COVID-19 Chest X-ray Image Classification: A Comparative Research of LBP-SVM and MobileNetV2
TL;DR
Because the application of data augmentation and regularization techniques, the training and validation loss curves of MobileNetV2 con-verged effectively, which in turn suppressed the issue of overfitting, confirming the superiority of MobileNetV2 in end-to-end feature extraction for COVID-19 imaging.
Abstract
: Since the COVID-19 outbreak, Reverse Transcription Polymerase Chain Re-action (RT-PCR) has limitations of long cycles and low sensitivity, making medical imaging (CT/X-ray) critical for early auxiliary diagnosis. However, manual image interpretation by radiologists is inefficient and prone to errors due to subjective factors.To address this, this paper compares two automated solutions using the CoronaHack dataset with preprocessing: LBP-based SVM and transfer learning-enabled MobileNetV2. Experimental results indicated that the optimized LBP-SVM model attained a test accuracy of 80.65%, whereas the MobileNetV2 model achieved a test accuracy of 95.36% and a recall rate of 98.40 — with only 4 out of 250 true positive samples being missed. Because the application of data augmentation and regularization techniques, the training and validation loss curves of MobileNetV2 con-verged effectively, which in turn suppressed the issue of overfitting. This confirms the superiority of MobileNetV2 in end-to-end feature extraction for COVID-19 imaging.