Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study
Journal:
arXiv
Published Date:
Dec 28, 2024
Abstract
This paper explores and enhances the application of Transfer Learning (TL)
for multilabel image classification in medical imaging, focusing on brain tumor
class and diabetic retinopathy stage detection. The effectiveness of TL-using
pre-trained models on the ImageNet dataset-varies due to domain-specific
challenges. We evaluate five pre-trained models-MobileNet, Xception,
InceptionV3, ResNet50, and DenseNet201-on two datasets: Brain Tumor MRI and
APTOS 2019. Our results show that TL models excel in brain tumor
classification, achieving near-optimal metrics. However, performance in
diabetic retinopathy detection is hindered by class imbalance. To mitigate
this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with
TL and traditional machine learning(ML) methods, which improves accuracy by
1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings
underscore the need for combining TL with resampling techniques and ML methods
to address data imbalance and enhance classification performance, offering a
pathway to more accurate and reliable medical image analysis and improved
patient outcomes with minimal extra computation powers.
