Quantum Filter based CNN for Improved Thyroid Nodule Classification

The need for accurate and early detection of thyroid nodules has become increasingly critical due to the rising incidence of thyroid cancer. This study presents an innovative automated system that leverages a quantum-inspired convolutional neural network to enhance the classification accuracy of thyroid nodules in ultrasound images. Traditional methods face challenges with limited datasets and complex feature extraction, which this research aims to overcome. By employing a quantum filter transformation, the proposed model extracts detailed and meaningful features from the ultrasound images, which are then processed by a classical neural network for classification. The system differentiates between benignant and malevolent nodules and further categorizes the nodules based on their suspicious characteristics. The classification approach delivers a comprehensive assessment of thyroid nodules. These performance metrics significantly outperform existing models, indicating a substantial advancement in thyroid detection and classification technology. The proposed model offers a promising and innovative solution, providing robust support for clinical diagnosis and potentially accelerating the diagnostic process for thyroid cancer. Our experimental results demonstrate that the proposed approach achieves an impressive accuracy of 98.59%.