Enabling Non-Invasive Diagnosis Of Thyroid Nodules With High Specificity And Sensitivity


Authors : Sai Maniveer Adapa; Sai Guptha Perla; Adithya Reddy. P

Volume/Issue : Volume 9 - 2024, Issue 1 - January

Google Scholar : http://tinyurl.com/5czk7dfu

Scribd : http://tinyurl.com/ycyk39sw

DOI : https://doi.org/10.5281/zenodo.10634167

Abstract : Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords : Thyroid Tumor Diagnosis, Ultrasound Images, Deep Learning, Machine Learning, Convolutional AutoEncoder, Support Vector Machine

Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords : Thyroid Tumor Diagnosis, Ultrasound Images, Deep Learning, Machine Learning, Convolutional AutoEncoder, Support Vector Machine

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