Publication Date: 2024/02/08
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
DOI: https://doi.org/10.5281/zenodo.10634167
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN1329.pdf
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