Classification of histological images of thyroid nodules based on a combination of Deep Features and Machine Learning
DOI:
https://doi.org/10.26415/2572-004X-vol5iss1p604-614Keywords:
Thyroid nodules, Papillary carcinoma, Follicular adenoma, Deep feature, Support Vector Machine learning, K Nearest Neighbor, Random Forest, supervised machine learning, transfer learning,Abstract
Background: Thyroid nodules are a prevalent worldwide disease with complex pathological types. They can be classified as either benign or malignant. This paper presents a tool for automatically classifying histological images of thyroid nodules, with a focus on papillary carcinoma and follicular adenoma. Methods: In this work, two pre-trained Convolutional Neural Network (CNN) architectures, VGG16 and VGG19, are used to extract deep features. Then, a principal component analysis was used to reduce the dimensionality of the vectors. Then, three machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, and Random Forest) were used for classification. These investigations were applied to our database collection, Results: The proposed investigations have been applied to our private database collection with a total of 112 histological images. The highest results were obtained by the VGG16 transfer deep feature and the SVM classifier with an accuracy rate equal to 100%.Downloads
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