Automatic Detection of Thyroid Nodules in Ultrasound Images using Convolutional Neural Networks for Early Cancer Diagnosis

Thyroid nodule detection in ultrasonic images mostly rely on conventional image processing techniques, which have major negative effects including high false positive and negative rates, limited generalizability over various patient populations, and reliance on hand feature extraction. By comparison, the proposed system greatly increases detection accuracy and dependability by using Convolutional Neural Networks (CNNs) for automatic feature extraction and classification. To improve model generalizing and fight overfitting, the system combines cutting-edge data augmentation techniques including rotation, scaling, and flips. It uses pre-trained architectures such as VGG16 and ResNet using transfer learning to enable fast adaptation to the thyroid nodule detection challenge even with small data. Before entering the CNN, extensive preprocessing methods include Gaussian filtering and adaptive histogram equalization improve image quality. The system outperforms existing methods and provides a strong tool for early thyroid cancer diagnosis since its results show accuracy of 90.7%, sensitivity of 88.3%, and specificity of 92.1%.