ThyroNet: A CNN-Based Intelligent Diagnostic Tool for Thyroid Cancer Detection

Abstract

Thyroid cancer is among the most common endocrine malignancies, and its early and accurate diagnosis is essential for effective treatment and improved patient outcomes. This project introduces ThyroNet, an intelligent, web-based diagnostic tool designed to assist in the detection and classification of thyroid cancer. ThyroNet utilizes Convolutional Neural Networks (CNNs) to analyze ultrasound images of thyroid nodules, enabling the differentiation between benign and malignant cases with high precision. The system features an intuitive interface that allows users to upload ultrasound images and receive real-time diagnostic predictions, offering a non-invasive and user-friendly alternative to traditional diagnostic methods. Additionally, ThyroNet provides visualizations of model predictions and confidence scores to support clinical interpretation and decision-making. By reducing reliance on unnecessary biopsies and assisting healthcare professionals in making informed decisions, ThyroNet aims to improve the efficiency and reliability of thyroid cancer diagnosis. This project demonstrates the potential of integrating deep learning and intuitive design to create impactful AI-driven healthcare solutions.