Thyroid Development Of A Deep Learning Model To Predict Thyroid Diseases Based On Medical Characteristics

Abstract

The thyroid gland secretes hormones that control metabolism. It is a vascular organ essential to human physiology. The two main conditions that influence it are hyperthyroidism and hypothyroidism, which cause aberrant hormone levels to be released, upsetting metabolic equilibrium. Although they are frequently noisy and hazy, blood tests for thyroid function are essential for diagnosis. In order to improve these tests for precise analytics and make the prediction of illness risk possible, data purification techniques are used. To predict a patient’s risk of thyroid disease, machine learning techniques such as logistic
regression, decision trees, and support vector machines, K-nearest neighbors, and artificial neural networks are used.
A online application makes it easier for users to enter data, which helps with illness prediction.The principal diagnostic modality for determining the likelihood of malignancy in thyroid nodules and directing the process of fineneedle aspiration is ultrasound (US).However, unneeded FNA and operations are widespread because of operatordependency and moderate to large interobserver heterogeneity in picture interpretation. Artificial intelligence
(AI)-based computer-aided diagnostic (CAD) systems have been introduced as a solution to this problem. By offering a uniform and correct interpretation of US features, these systems hope to cut down on pointless FNA procedures. This paper examines future directions for tailored and optimal nodule care while providing an overview of the state-of-the-art AI-based CAD systems for thyroid nodules.