Abstract
The early diagnosis of malignancy by ultrasound, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) is still limited. Therefore, the diagnostic efficacy could be improved from Machine Learning (ML), which mainly based on deep learning and convolutional neural networks. In this work, we reviewed ML-based imaging examinations ultrasound, CT and MRI could evaluate early diagnosis value of small size tumor. Besides, ultrasound could distinguish primary tumors from metastatic tumors, CT could also play a role in tumor risk classification, and MRI could also predict the pathological grade and enzyme mutation status of malignancy, which can be used to predict early survival and guide clinical decision-making. Therefore, we believe that ML could be added to improve the accuracy of diagnosis in patients with suspected tumor imaging examinations.
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