Classification of Thyroid Carcinoma in Whole Slide Images Using Cascaded CNN

• Published in: IEEE access 2021, Vol.9, p.88429-88438
• Main Authors: El-Hossiny, Ahmed S., Al-Atabany, Walid, Hassan, Osama, Soliman, Ahmed M., Sami, Sherif A.
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Classification; Computer architecture; convolutional neural network; Convolutional neural networks; data augmentation; histopathology whole slide imaging; Image classification; Image color analysis; Thyroid; Thyroid cancer; thyroid carcinoma; Training; Tumors; Whole slide images
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3076158

The objective of this research is to build a "Whole Slide Images" classification system using Convolutional Neural Network (CNN). This system is capable of classifying Thyroid tumors into three types: Follicular adenoma, follicular carcinoma, and papillary carcinoma. Furthermore, the cascaded CNN technique is additionally employed to classify the classified follicular carcinoma into four subclasses: follicular carcinoma, papillary follicular variant, well-differentiated follicular carcinoma, and Poorly-differentiated follicular carcinoma. Results of the proposed CNN architecture showed effective classification of Thyroid carcinoma in the whole slide images with an overall accuracy of 94.69%. In the first classification stage, the images are classified into either one of three main types with an overall accuracy of 98.74%, while in the second classification stage, using the cascaded CNN, accuracy was 95.90% for further sub-classification into four sub-classes. Our cascaded CNN outperformed the accuracy of other studies due to splitting classification process of the thyroid into two stages which reduces the number of classes in each stage.