We will further revise our models, hoping to achieve a higher sensitivity and specificity.Ĭirculating tumour cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. The sensitivity and specificity of recognition reached 90.3 and 91.3%, respectively. About 1300 cells were used for training and the others were used for testing. We took 2300 cells from 600 patients for training and testing. Machine learning algorithms are implemented using convolutional neural network deep learning networks for training. Subsequently, traditional image recognition methods and machine learning were used to identify CTCs. The images of CTCs were then segmented by image denoising, image filtering, edge detection, image expansion and contraction techniques using python’s openCV scheme. After immunofluorescence staining, each picture presented a positive CTC cell nucleus and several negative controls. First, we collected the CTC test results of 600 patients. So, we use machine learning to identify CTCs. Medical image recognition based on machine learning can effectively reduce the workload and improve the level of automation. This not only requires the participation of experienced pathologists, but also easily causes artificial misjudgment. Circulating tumor cells enrichment and screening can be automated, but the final counting of CTCs currently requires manual intervention. Meanwhile, because of the application of a lightweight network in different hardware acceleration platform, the detection time of the CTC image in a single view can be less than 12s.Ĭirculating tumor cells (CTCs) derived from primary tumors and/or metastatic tumors are markers for tumor prognosis, and can also be used to monitor therapeutic efficacy and tumor recurrence. In contrast to other combinations, we detect CTC in diverse clinical CTC images, the joint application of ISS algorithm and CTCNet achieves an outstanding system performance with accuracy up to 94.03% in the whole view images. And we got the highest recognition accuracy of 97.95% on CTCNet, which even beyond the VGGNet with deeper layers. In order to evaluate the performance of the proposed 7-layer CTCNet, we compared CTCNet with SVM, BP neural network, AlexNet and VGGNet by using the dataset of 12312 samples from 30 patients, among them, there were 12 patients with early cancer and 18 patients with advanced cancer. In the experiment, we first verify the efficiency of proposed ISS algorithm by compared with Original Selective Search (OSS), we found that the number of candidate boxes reduced from 549 to 16 and the time consuming reduced by 0.3s after adopting the ISS algorithm. During this process, all algorithms are accelerated through the GPU and NPU hardware platform, which further improves the detection speed of the system. All of the eligible areas are evaluated with a self-designed neural network called CTCNet, resulting in efficient recognition of circulating tumor cells. After fluorescent staining process, images obtained from sampling needle will be processed by the ISS algorithm for candidate region preselection. CTCs can be collected by a sampling needle with EpCAM antibody which can specifically bind to tumor cells. In this paper, we propose Imporved Selective Search (ISS) algorithm and CTCNet based on Circulating Tumor Cell (CTC) technology to improve the cancer images’ detection efficiency. Nowadays, the intelligent detection of tumor cell images is commonly adopted in cancer diagnosis. However, manual interpretation of the original images is time consuming and labor consuming. Doctors can get original images of the sick organs with the assistance of medical technology. Cancer has become one of the greatest threats for human life.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |