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
