卷积神经网络
医学
人工智能
分割
深度学习
模式识别(心理学)
放射科
乳腺癌
肺
癌症
计算机科学
内科学
作者
Mehmet Ali Gültekin,Abdusselim Adil Peker,Ayşe Betül Oktay,Hacı Mehmet Türk,Dilek Hacer Çeşme,Abdallah Tm Shbair,Temel Fatih Yılmaz,Ahmet Hilmi Kaya,Ayşe İrem Yasin,Mesut Şeker,Alpaslan Mayadağlı,Alpay Alkan
摘要
Abstract Purpose Metastases are the most common neoplasm in the adult brain. In order to initiate the treatment, an extensive diagnostic workup is usually required. Radiomics is a discipline aimed at transforming visual data in radiological images into reliable diagnostic information. We aimed to examine the capability of deep learning methods to classify the origin of metastatic lesions in brain MRIs and compare the deep Convolutional Neural Network (CNN) methods with image texture based features. Methods One hundred forty three patients with 157 metastatic brain tumors were included in the study. The statistical and texture based image features were extracted from metastatic tumors after manual segmentation process. Three powerful pre‐trained CNN architectures and the texture‐based features on both 2D and 3D tumor images were used to differentiate lung and breast metastases. Ten‐fold cross‐validation was used for evaluation. Accuracy, precision, recall, and area under curve (AUC) metrics were calculated to analyze the diagnostic performance. Results The texture‐based image features on 3D volumes achieved better discrimination results than 2D image features. The overall performance of CNN architectures with 3D inputs was higher than the texture‐based features. Xception architecture, with 3D volumes as input, yielded the highest accuracy (0.85) while the AUC value was 0.84. The AUC values of VGG19 and the InceptionV3 architectures were 0.82 and 0.81, respectively. Conclusion CNNs achieved superior diagnostic performance in differentiating brain metastases from lung and breast malignancies than texture‐based image features. Differentiation using 3D volumes as input exhibited a higher success rate than 2D sagittal images.
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