A Deep Learning Study on Osteosarcoma Detection from Histological Images
- URL: http://arxiv.org/abs/2011.01177v1
- Date: Mon, 2 Nov 2020 18:16:17 GMT
- Title: A Deep Learning Study on Osteosarcoma Detection from Histological Images
- Authors: D M Anisuzzaman, Hosein Barzekar, Ling Tong, Jake Luo, Zeyun Yu
- Abstract summary: The most common type of primary malignant bone tumor is osteosarcoma.
CNNs can significantly decrease 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.
- Score: 6.341765152919201
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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