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 CTscanned 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.INDEX TERMS CNNs, Lung nodule, Metastatic cancer, Osteosarcoma, SSD-VGG16 I. INTRODUCTIONOsteosarcoma [1-5] is one of the most common types of bone cancer about 28% of all cancer [6][7][8]. This bone cancer can metastasize to other organs, especially the lungs. The majority of osteosarcoma patients suffered clinically detectable metastases from bone to the lung [9][10][11]. Regularly, the lung nodule detection standard method is to read the computed tomography (CT) scan file. In Thailand, the recorded cases of this disease were very rare therefore CTscanned images are limited. As the result, doctors are not familiar with this disease and find it difficult to diagnose. According to the previous studies, the rate of reading nodules in the lung metastasis group had an error rate between 29 to 42 percent [12][13][14][15][16][17][18][19][20][21][22]. This high error rate may result in missed diagnostics, particularly in false-negative cases. A false negative prediction leads to delayed treatment causing difficulty in curing the patient.Computer-aided diagnosis (CAD) [23][24][25][26][27] is an advanced technology to help radiologists and oncologists to read and identify the nodules from the CT-scanned image of the patient. CAD can be categorized into image processing and machine

RETRACTED: Lung Nodule Classification Using Deep Learning Algorithms and Feature Fusion

“…In addition, for nodule object detection he has used RCNN and YOLOv3 architecture. Chanunya Loraksa [2] has used the Single Shot Detection (SSD) framework and which is combined with the VGG16 backbone for lung nodule classification. He has collected real lung nodule CT images from Lerdsin Hospital.…”

Advancements in Image Enhancement and Attention based efficientdet Optimization Classifier for Precise Osteosarcoma Lung Nodule Detection

“…This approach enables a more granular assessment of the algorithm's performance, facilitating improvements in both detection quantity and quality across the entire dataset. The proposed optimized A-EfficientDet is compared with exiting methods such as MSANet (15) , TSCNN (16) and SSD-VGG16 (23) depends on performance matrix such as precision, recall, f-measure and accuracy. In the evaluation process, a corrected predicted nodule is considered a True Positive (TP) outcome.…”

“…The SIEMENS SOMATOM DEFINITION 64 machines at Lerdsin Hospital in Thailand provided DICOM files from which CT-scanned PNG-like images were extracted. (23) The utilization of these images for research has been approved by the Human Research Ethics Committee of Thammasat University (Science) (HREC-TUSc) and the RECof Lerdsin Hospital, Department of Medical Services, Ministry of Public Health in Thailand. The collection contains images from 202 individuals at Lerdsin Hospital who were diagnosed with OS.…”

An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans

“…For instance, Lin et al [22] proposed an automatic lesion detection model based on SSD for lung metastases caused by lung cancer in low-resolution SPECT bone scan images. Loraksa et al [23] proposed the SSD-VGG16 model for detecting nodules of osteosarcoma metastasis to the lungs, enhancing the ability to extract nodules and detection accuracy. Mammeri et al [24] utilized YOLO V7 for lung nodule detection and introduced a multiclass classification approach.…”