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 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.