A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images

Sci Rep. 2024 Oct 25;14(1):25434. doi: 10.1038/s41598-024-75549-0.

Abstract

This study proposes a new deep-learning approach incorporating a superfluity mechanism to categorize knee X-ray images into osteoporosis, osteopenia, and normal classes. The superfluity mechanism suggests the use of two distinct types of blocks. The rationale is that, unlike a conventional serially stacked layer, the superfluity concept involves concatenating multiple layers, enabling features to flow into two branches rather than a single branch. Two knee datasets have been utilized for training, validating, and testing the proposed model. We use transfer learning with two pre-trained models, AlexNet and ResNet50, comparing the results with those of the proposed model. The results indicate that the performance of the pre-trained models, namely AlexNet and ResNet50, was inferior to that of the proposed Superfluity DL architecture. The Superfluity DL model demonstrated the highest accuracy (85.42% for dataset1 and 79.39% for dataset2) among all the pre-trained models.

Keywords: Deep learning; Knee; Osteoporosis/osteopenia; Superfluity; X-ray images.

MeSH terms

  • Aged
  • Bone Diseases, Metabolic* / diagnostic imaging
  • Deep Learning*
  • Female
  • Humans
  • Knee / diagnostic imaging
  • Knee Joint / diagnostic imaging
  • Male
  • Middle Aged
  • Osteoporosis* / diagnostic imaging
  • Radiography / methods