Classification of Thyroid Cancer Subtypes With Imagenet Pretrained CNNS

This study applies interpretivism, a deductive method, and a descriptive design to prepared Convolutional Neural Networks (for identifying thyroid cancer subgroups from histopathology pictures. Utilized is secondary data made up of several annotated photos. Strong subtype categorization performance is displayed by the fine-tuned CNN, which achieves high accuracy, preciseness, and recall. Key diagnostic aspects are clarified by interpretability metrics like Grad-CAM as well as Layer-wise Resonance Propagation. Potential discrepancies in forecasts are addressed by bias analysis as well as fairness measures. Comparing performance to traditional approaches reveals superiority. A prospective clinical verification study, research into cutting-edge deep learning structures, and dataset expansion are suggested. Future research will focus on molecular markers along with multi-modal interfacing. This study improves the diagnosis of thyroid cancer by providing an accurate method for subtype differentiation