A Deep Learning Study on Osteosarcoma Detection from Histological Images

TL;DR

This study applies transfer learning with CNNs to histological images for osteosarcoma detection, achieving high accuracy and aiding in diagnosis with deep learning methods.

Contribution

It adapts pre-trained CNNs like VGG19 and Inception V3 for osteosarcoma histology classification, demonstrating state-of-the-art accuracy.

Findings

VGG19 achieved 96% accuracy in binary classification.

Transfer learning improved detection performance.

Models effectively distinguish necrotic from healthy tissues.

Abstract

In the U.S, 5-10\% of new pediatric cases of cancer are primary bone tumors. The most common type of primary malignant bone tumor is osteosarcoma. The intention of the present work is to improve the detection and diagnosis of osteosarcoma using computer-aided detection (CAD) and diagnosis (CADx). Such tools as convolutional neural networks (CNNs) can significantly decrease the surgeon's workload and make a better prognosis of patient conditions. CNNs need to be trained on a large amount of data in order to achieve a more trustworthy performance. In this study, transfer learning techniques, pre-trained CNNs, are adapted to a public dataset on osteosarcoma histological images to detect necrotic images from non-necrotic and healthy tissues. First, the dataset was preprocessed, and different classifications are applied. Then, Transfer learning models including VGG19 and Inception V3 are used and trained on Whole Slide Images (WSI) with no patches, to improve the accuracy of the outputs. Finally, the models are applied to different classification problems, including binary and multi-class classifiers. Experimental results show that the accuracy of the VGG19 has the highest, 96\%, performance amongst all binary classes and multiclass classification. Our fine-tuned model demonstrates state-of-the-art performance on detecting malignancy of Osteosarcoma based on histologic images.