Thyroid Gland Classification With Diagnosis System Using TL Algorithms and Quantum Computing

Thyroid nodules are a sign of several thyroid disorders, and medical image analysis is essential to their early identification and diagnosis. In existing framework using algorithms for machine learning and QC, such Random Forest and Support Vector Machine to classify the thyroid nodules. In this work, we suggest a novel use of transfer learning algorithms for the classification of thyroid nodules. Transfer learning makes use of neural network models that have already been trained on sizable datasets to modify them for particular tasks that require less data. To extricate significant elements from thyroid ultrasound filters, we utilize a forefront convolutional brain organization (CNN) that has been pre-prepared on different clinical pictures. Using a specific dataset of images of thyroid nodules and Quantum computing, the model is refined to maximize its performance for precise classification.