Lightweight Residual Network for The Classification of Thyroid Nodules

2019/11/16 by Ponugoti Nikhila, Sabari Nathan, Nikhila, Ponugoti +9
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #AI in cancer detection #Biomedical Text Mining and Ontologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Thyroid Cancer Diagnosis and Treatment #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1911.08303

openalex publication_date 2019/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Ultrasound is a useful technique for diagnosing thyroid nodules. Benign and malignant nodules that automatically discriminate in the ultrasound pictures can provide diagnostic recommendations or, improve diagnostic accuracy in the absence of specialists. The main issue here is how to collect suitable features for this particular task. We suggest here a technique for extracting features from ultrasound pictures based on the Residual U-net. We attempt to introduce significant semantic characteristics to the classification. Our model gained 95% classification accuracy.

Citations

• An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

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