ECNN-TDP: A Novel Convolutional Neural Network Model for Improved Thyroid Disease Classification

Thyroid disease is among the most common endocrine illnesses and necessitates early and correct diagnosis to avoid serious health consequences. This paper presents ECNN-TDP, an Enhanced Convolutional Neural Network model that was designed for the enhanced classification of thyroid diseases from medical images and clinical information. The suggested model utilizes cutting-edge deep learning methods like transfer learning, attention mechanisms, batch normalization, dropout, and advanced activation functions to improve its prediction accuracy and generalization ability.ECNN-TDP is compared to benchmark models such as SCNN and TLCNN and is shown to perform better in fundamental measures of performance such as accuracy, precision, recall, and F1-score—especially with larger dataset sizes. Although the model takes longer to train because it has a more complicated architecture, it makes up for this by making much quicker and more precise real-time predictions, rendering it an auspicious clinical tool.The findings affirm that the incorporation of contemporary deep learning techniques into CNN architecture significantly improves diagnostic efficiency for thyroid disorders. ECNN-TDP is a robust and efficient solution for early detection and classification of thyroid diseases. Optimizing its architecture further and verifying its performance in real-world clinical settings will be the focus of future work to maximize its potential in healthcare. Keywords: Thyroid Disease, Convolutional Neural Network, Deep Learning, Transfer Learning, Medical Imaging, Attention Mechanism