Open Access

ARTICLE

A Novel Technique for Detecting Various Thyroid Diseases Using Deep Learning

by Soma Prathibha^1,*, Deepak Dahiya², C. R. Rene Robin³, Cherukuru Venkata Nishkala⁴, S. Swedha⁵

1 Department of Information Technology, Sri Sai Ram Engineering College, Chennai, 600044, India
2 College of Computer and Information Sciences (CCIS), Majmaah University, Majmaah, 11952, Kingdom of Saudi Arabia
3 Department of Computer Science and Engineering, Sri Sai Ram Engineering College, Chennai, 602109, India
4 Associate Solutions Engineer, Pegasystems, Chennai, 600044, India
5 Systems Engineer, Infosys Ltd., Chennai, 600119, India

* Corresponding Author: Soma Prathibha. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 199-214. https://doi.org/10.32604/iasc.2023.025819

Received 06 December 2021; Accepted 12 February 2022; Issue published 06 June 2022

Abstract

Thyroid disease is a medical condition caused due to the excess release of thyroid hormone. It is released by the thyroid gland which is in front of the neck just below the larynx. Medical pictures such as X-rays and CT scans can, however, be used to diagnose it. In this proposed model, Deep Learning technology is used to detect thyroid diseases. A Convolution Neural Network (CNN) based modified ResNet architecture is employed to detect five different types of thyroid diseases namely 1. Hypothyroid 2. Hyperthyroid 3. Thyroid cancer 4. Thyroiditis 5. Thyroid nodules. In the proposed work, the training method is enhanced using dual optimizers for better accuracy and results. Keras, a Python library that is high level runs as the main part of the Tensor Flow framework. It is used in the proposed work to implement deep learning techniques. The comparative analysis of the proposed model and the existing work helps to show that there is a great improvement in the performance metrics in classifying the type of thyroid disease. By applying Adam and SGD (Stochastic Gradient Descent) optimizers in the training phase of the proposed model it was identified that these increase the operational efficiency of the modified ResNet model. After retraining the model with SGD, the modified ResNet provides more accuracy of about 97% whereas the basic ResNet architecture attains 94% accuracy. A web-based framework is also developed which yields the type of thyroid disease as the output for a given input scanned image of the system.

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Keywords

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