Development of the Osteosarcoma Lung Nodules Detection Model Based on SSD-VGG16 and Competency Comparing With Traditional Method

Abstract

Osteosarcoma nodule that metastasized to the patient's lungs was difficult to detect due to limited cases caused by its rarity. The traditional method for finding lung nodules is manually done by radiologists by looking at CT-scanned images. As a result, the error rate for reading lung metastasized nodules ranged from 29 to 42 percent, while the permissible mistake rate for reading should be less than 29 percent. Advanced computer-aid techniques such as image processing and machine learning can help doctors to identify the Osteosarcoma lung nodules easier and more accurately. Convolutional Neural Networks (CNNs) are promising techniques since they could be trained by experienced radiologists. Nodule location and size information was critical for treatments that were obtained by object detector CNNs models. In this research, the Single Shot Detection (SSD) framework combined with the VGG16 backbone, SSD-VGG16, was implemented to obtain bounding box locations and sizes when each box represents one Osteosarcoma nodule with the confidence score. The SSD-VGG16 was selected due to its superior performance. The patient's CT-scanned images dataset collected from 202 patient cases was provided by Lerdsin hospital and used for training and validating the SSD-VGG16 model. The trained SSD-VGG16 model was trained based on two loss functions which are class confidence and location losses. Then, the trained model experimented with unseen CT-scanned images. The performance scores were calculated. The Result was analyzed and concluded. Finally, SSD-VGG16 shows the ability to detect and locate the nodules efficiently and has less error compared to the traditional method.