Application of the convolutional neural networks and supervised deep-learning methods for osteosarcoma bone cancer detection

Abstract

Osteosarcoma is a cancerous tumor that occurs in bones. Although it can occur in any bone, it often occurs in long bones such as arms and legs. The exact cause of this cancerous tumor is still unknown, but according to experts, it occurs due to the deoxyribonucleic acid (DNA) mutations inside the bones. This creates immature, irregular, diseased bone and can destroy healthy body tissue. About 75 out of 100 people who have osteosarcoma can be cured if the cancer is not dispersed to the additional body parts. The bone X-ray is the initial test when a bone tumor is suspected. X-ray and imaging tests are the best way to identify osteosarcoma from the bones. A biopsy is the suggested method that can make a definitive diagnosis. This is a time-consuming and difficult procedure that can be automated. We propose several supervised deep-learning methods and select the most suitable model. The selection is made through the weightage from the users’ data to detect bone cancer. We show the model selected meets the expectations with the highest accuracy 90.36% using the residual neural network(ResNet101) algorithm and 89.51% precision in the prediction tasks.

Keywords

• Convolutional neural networks
• Supervised deep-learning
• Osteosarcoma
• Cancerous tumor
• Bone cancer
• ResNet