Automatic classification of spinal osteosarcoma and giant cell tumor of bone using optimized DenseNet

Abstract

Objective: This study aims to explore an optimized deep-learning model for automatically classifying spinal osteosarcoma and giant cell tumors. In particular, it aims to provide a reliable method for distinguishing between these challenging diagnoses in medical imaging.

Methods: This research employs an optimized DenseNet model with a self-attention mechanism to enhance feature extraction capabilities and reduce misclassification in differentiating spinal osteosarcoma and giant cell tumors. The model utilizes multi-scale feature map extraction for improved classification accuracy. The paper delves into the practical use of Gradient-weighted Class Activation Mapping (Grad-CAM) for enhancing medical image classification, specifically focusing on its application in diagnosing spinal osteosarcoma and giant cell tumors. The results demonstrate that the implementation of Grad-CAM visualization techniques has improved the performance of the deep learning model, resulting in an overall accuracy of 85.61%. Visualizations of images for these medical conditions using Grad-CAM, with corresponding class activation maps that indicate the tumor regions where the model focuses during predictions.

Results: The model achieves an overall accuracy of 80% or higher, with sensitivity exceeding 80% and specificity surpassing 80%. The average area under the curve AUC for spinal osteosarcoma and giant cell tumors is 0.814 and 0.882, respectively. The model significantly supports orthopedics physicians in developing treatment and care plans.

Conclusion: The DenseNet-based automatic classification model accurately distinguishes spinal osteosarcoma from giant cell tumors. This study contributes to medical image analysis, providing a valuable tool for clinicians in accurate diagnostic classification. Future efforts will focus on expanding the dataset and refining the algorithm to enhance the model's applicability in diverse clinical settings.

Keywords: Automatic classification and diagnosis; DenseNet; Giant cell tumors; Self-attention mechanism; Spinal osteosarcoma.

Fig. 1

Dense block structure. This structure comprises every layer consisting of BN-ReLU-Conv (1 × 1) and BN-ReLU-Conv (3 × 3), with batch normalization and rectified linear units applied before each convolutional layer.

Fig. 2

SMSDNet module architecture. A multi-scale DenseNet classification model with a self-attention mechanism is utilized for feature extraction and classification of spinal osteosarcoma and giant cell tumors in medical images.

Fig. 3

Self-attention dense block. This mechanism's heart is the distribution of weights to each output from the preceding layer before concatenation.

Fig. 4

Visualization of spinal osteosarcoma and giant cell tumor images using Grad-CAM on the trained model. (a) Original image showing spinal osteosarcoma, (b) Original image showing spinal osteosarcoma, (c) Algorithm activation map showing giant cell tumor, (d) Class activation map showing giant cell tumor, (e) Showing Raw image of spinal osteosarcoma, (f) algorithm activation map showing giant cell tumor. [Note: High-intensity visuals (blue and green) reflect the regions of interest that our model focuses on when making predictions]. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

ROC Curves of Image Group Classification Models for Spinal Osteosarcoma and Giant Cell Tumor. Note: (a) represents the ROC curve for the spinal osteosarcoma training group, (b) for the spinal osteosarcoma testing group, (c) for the giant cell tumor training group, and (d) for the giant cell tumor testing group.