Keywords:
Thyroid Cancer, Convolutional Neural Network, Ultrasound images, The Thyroid Digital Image Database (TDID), VGG-16

  • Abstract

    Thyroid cancer is notably more common in women, particularly between the ages of 30 and 60, highlighting the need for accurate diagnostic techniques. This study explores the application of the VGG16 deep Convolutional Neural Network (CNN) architecture to enhance the classification accuracy of thyroid nodules detected through ultrasound imaging. The Thyroid Digital Image Database (TDID), a publicly available dataset, is utilized for training and validating the proposed model. By fine-tuning the VGG16 CNN, we aim to improve its capability to differentiate between benign and malignant nodules effectively. Through rigorous experimentation and validation, the results demonstrate that the fine-tuned VGG16 CNN significantly outperforms traditional classification methods, achieving higher predictive accuracy and reducing the potential for human error. This advancement in automated classification not only streamlines the diagnostic process but also ensures more consistent results, ultimately aiding radiologists in clinical decision-making. The findings indicate that integrating deep learning techniques, such as the VGG16 CNN, into clinical practice can revolutionize the approach to thyroid nodule assessment, enhancing early detection and treatment outcomes. This is particularly significant for women who are disproportionately affected by thyroid disorders. Overall, this research contributes to the growing field of artificial intelligence in healthcare, underscoring the importance of machine learning in improving diagnostic accuracy, optimizing patient care, and paving the way for personalized medicine strategies in the management of thyroid cancer.

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Copyright (c) 2024 Preeti Katiyar, Krishna Singh

How to cite

Katiyar, P., & Singh, K. (2024). A fine-tuned deep learning model for thyroid cancer classification in ultrasound Images. Multidisciplinary Science Journal, (| Accepted Articles). Retrieved from https://malque.pub/ojs/index.php/msj/article/view/7174
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