MAFMv3: An automated Multi-Scale Attention-Based Feature Fusion MobileNetv3 for spine lesion classification

Abstract

Spine lesion classification is a crucial task in medical imaging that plays a significant role in the early diagnosis and treatment of spinal conditions. In this paper, we propose an MAFMv3 (Multi-Scale Attention Feature Fusion MobileNetv3) model for automated spine lesion classification, which builds upon MobileNetv3, incorporating Attention and Atrous Spatial Pyramid Pooling (ASPP) modules to enhance focus on lesion regions and capture multi-scale features. This novel architecture uses raw, normalized, and histogram-equalized images to generate a comprehensive 3D feature map, significantly improving classification performance. Preprocessing steps include Histogram Equalization, and data augmentation techniques are applied to expand the dataset and enhance model generalization. The proposed model is evaluated on the VinDr-SpineXR publicly available dataset. The MAFMv3 model achieves state-of-the-art results with an accuracy of 96.81%, precision of 98.38%, recall of 97.95%, F1-score of 98.15%, and AUC of 99.98%, demonstrating its potential for clinical applications in medical imaging. Future work will focus on further optimizations and validating the model in real-world clinical environments to enhance its diagnostic impact.