Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study

Abstract

Objective: In this study, we exploited a VGG-16 deep convolutional neural network (DCNN) model to differentiate papillary thyroid carcinoma (PTC) from benign thyroid nodules using cytological images.Methods: A pathology-proven dataset was built from 279 cytological images of thyroid nodules. The images were cropped into fragmented images and divided into a training dataset and a test dataset. VGG-16 and Inception-v3 DCNNs were trained and tested to make differential diagnoses. The characteristics of tumor cell nucleus were quantified as contours, perimeter, area and mean of pixel intensity and compared using independent Student's t-tests.Results: In the test group, the accuracy rates of the VGG-16 model and Inception-v3 on fragmented images were 97.66% and 92.75%, respectively, and the accuracy rates of VGG-16 and Inception-v3 in patients were 95% and 87.5%, respectively. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules, which were 61.01±17.10 vs 47.00±24.08, p=0.000, 134.99±21.42 vs 62.40±29.15, p=0.000, 1770.89±627.22 vs 1157.27±722.23, p=0.013, 165.84±26.33 vs 132.94±28.73, p=0.000), respectively.Conclusion: In summary, after training with a large dataset, the DCNN VGG-16 model showed great potential in facilitating PTC diagnosis from cytological images. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules.

Emerging role of deep learning‐based artificial intelligence in tumor pathology

“…In liquid‐based urine cytology, AI could separate high‐grade urothelial carcinoma (HGUC) and suspicious HGUC from other lesions based on cell level‐features [28] or WSI level‐features [29]. AI also performed showed promising ability in the differential diagnosis for thyroid tumors on the basis of cytological images [30]. In summary, DL‐based AI has shown promise in histological and cytology diagnosis for many kinds of tumors.…”

Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis

“…Landmark studies have tended to use a version of GoogleNet called Inception v3; however, AlexNet or other simple models such as VGG are popular in the analysis of medical data [ 23 ]. Indeed, VGG-16 has been reported to outperform Inception v3 in classifying medical images [ 24 , 25 ]. It has also accurately identified speckle patterns for the classification of hepatic steatosis [ 26 ].…”

Automatic Assessment of ASPECTS Using Diffusion-Weighted Imaging in Acute Ischemic Stroke Using Recurrent Residual Convolutional Neural Network

“…VGG16 [ 17 ] and Inception V3 [ 19 ] are two of the popular deep neural networks that have been shown to be very efficient in image classification. Although these networks, which were pre-trained with large-scale data from ImageNet [ 20 ], have been widely applied to medical image analysis in combination with transfer learning and fine tuning technique [ 44 , 45 , 46 , 47 , 48 ], the effectiveness of the transfer learning method using these deep networks is debatable [ 49 ] because medical images, such as MRI and CT, are very different from the images in ImageNet, which are mostly natural images, and the high-level features that are learned during training of medical images can be very different. In addition, the application of transfer learning using these deep networks may need careful consideration depending on the training condition, such as the number of training datasets, because these complex neural networks generally require a large amount of training data to be effectively trained.…”