Design a Workflow for the Application of Machine Learning in Diagnosis of Cancer Metastasis
DOI:
https://doi.org/10.47611/jsrhs.v11i3.3774Keywords:
Artificial Intelligence (AI), Machine learning, Cancer malignancy, Cancer diagnosisAbstract
Benign tumors can turn into malignant tumors if they are metastatic. Pathological analysis of cancer tissue is the major method for the diagnosis of cancer malignancy and has been widely used for decades. The analysis is based on the specific features of the malignant tissues with metastatic cancer cells. Nevertheless, the diagnosis fully relies on experts’ efforts, which is a time-consuming process. Also, the decision based on experts’ experiences could be empirical. In this mini review work, we introduced the definition and the diagnosis of cancer malignancy and pointed out the disadvantages of the traditional diagnosis. To improve the efficiency and accuracy of the diagnosis, we proposed a novel workflow for the diagnosis of cancer malignancy. In this workflow, we integrated the in vitro primary cancer cell culture and the machine learning algorithms. After training with big data that consists of images with known features of malignancy status, the machine learning algorithms can recognize the cancer malignancy to perform the diagnosis for cancer patients.
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