Performance Benchmarking of Deep Learning Techniques for Classifying Thyroid Tumors in Ultrasound Scans*

Abstract

One of the most important aspects of global health today is the concern for cancer, including thyroid cancer, which needs special attention related to early detection and diagnosis in order to treat it effectively. This paper focuses on classifying thyroid tumors using deep learning techniques and proposes a custom-designed convolutional neural network (CNN) architecture specifically optimized for ultrasound images. CNN is used to classify thyroid nodules as benign or malignant. The data set consists of ultrasound images which are publicly available. Several image processing approaches are performed in order to improve model performance. Diagnostic evaluation of the proposed model is performed using classification metrics such as accuracy, precision, recall, F1 score, and AUC-ROC curve analysis. The findings indicate that deep learning approaches are able to accurately classify different types of thyroid tumors as well as surpass the performance of conventional machine learning methods. Moreover, the study displays visual analyses such as the accuracy/loss curve, confusion matrix plot, and ROC-AUC curve plots to show the model performance more comprehensively. Explainability is further enhanced through Grad-CAM and SHAP visualizations, aiding in clinical interpretation.

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