Detection of Osteosarcoma on Bone Radiographs Using Convolutional Neural Networks

Abstract

We propose a computer-aided diagnosis system based on convolutional neural networks (CNNs) for the identification of osteosarcoma on bone radiographs. The CNN should indicate regions of the image that may contain tumors. In order to indicate these regions on the image, we propose to split the image in windows and individually classify them by using a CNN. Techniques for pre-processing, such as window exclusion and labeling, are proposed. Two CNNs are compared in the proposed system. The first one is trained from scratch, while the second one is a pre-trained CNN (VGG16). The CNNs are compared to four machine learning models that use features extracted from the image windows as inputs: multilayer perceptron (MLP), decision tree, random forest, and MLP with feature selection. In the experiments, the best performance was obtained by the pre-trained CNN.