Transfer learning with multiple convolutional neural networks for soft tissue sarcoma MRI classification

In this paper, we investigate the classification of two soft tissue sarcoma subtypes within a multi-modal medical dataset based on three pre-trained deep convolutional networks of the ImageNet challenge. We use multiparametric MRI's with histologically confirmed liposarcoma and leiomyosarcoma. Furthermore, the impact of depth on fine-tuning for medical imaging is highlighted. Therefore, we fine-tune the AlexNet along with deeper architectures of the VGG. Two configurations with 16 and 19 learned layers are fine-tuned. Experimental results reveal a 97.2% of classification accuracy with the AlexNet CNN, while better performance has been achieved using the VGG model with 97.86% and 98.27% on VGG-16-Net and VGG-19-Net, respectively. We demonstrated that depth is favorable for STS subtypes differentiation. Addionally, deeper CNN's converge faster than shallow, despite, fine-tuned CNN's can be used as CAD to help radiologists in decision making.