Revolutionizing Thyroid Disease Forecasting with API Enhanced Convolutional Neural Networks

Abstract

The healthcare industry utilizes the progress of computational biology, enabling the aggregation of previously stored patient data for predicting and anticipating medical conditions. Hormones produced by the thyroid gland control metabolism, growth, and development. Death rates from thyroid disorders are decreased by early identification. Radiologists and pathologists typically diagnose thyroid illness, and this process strongly relies on their training and knowledge. This paper provides deep learning-driven algorithms helps to identify thyroid problems automatically, supporting clinicians' diagnostic choices and lowering the incidence of false-positive diagnoses made by humans. The proposed model classifies various thyroid illnesses using two pre-operative medical imaging modalities includes normal, multi-nodular goitre cystic, thyroiditis, adenoma, and cancer. In order to distinguish between the various disease types and creates a diagnostic model for thyroid disease based on cutting-edge deep convolutional neural network (CNN) architecture. The model performed exceptionally well on both medical image sets, achieving an accuracy of 97.2% for CT scans and 94.2% for ultrasound images. This is a significant improvement over previous models, and it has the potential to revolutionize the way that medical diagnoses are made. The experimental results highlight the deep learning model's feasibility and highlight its potential clinical uses by proving that the chosen CNN can adapt to both visual modalities.