# A Deep Learning Study on Osteosarcoma Detection from Histological Images

arXiv: https://arxiv.org/abs/2011.01177 · HTML: https://mlsanity.com/paper/2011.01177/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Multi-modal Wound Classification using Wound Image and Location by Deep Neural Network](https://mlsanity.com/paper/2109.06969/) (2021)
- [Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma](https://mlsanity.com/paper/2204.13838/) (2022)
- [HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis](https://mlsanity.com/paper/2508.11181/) (2025)
- [Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction](https://mlsanity.com/paper/2208.04910/) (2022)
- [Osteosarcoma Tumor Detection using Transfer Learning Models](https://mlsanity.com/paper/2305.09660/) (2023)
- [Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images](https://mlsanity.com/paper/2411.00832/) (2024)
- [Radiomics-Integrated Deep Learning with Hierarchical Loss for Osteosarcoma Histology Classification](https://mlsanity.com/paper/2601.09416/) (2026)

## Abstract (arXiv)

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.