DensNet121 and Improved Hippopotamus Optimization Algorithm to Diagnosis Thyroid Nodules

Abstract

The diagnosis of thyroid nodules remains a challenge due to the limitations of conventional imaging techniques. This paper aims to improve the accuracy and efficiency of thyroid nodule diagnosis. The proposed densnet121-IHOA model is a good solution to the diagnostic accuracy problem. The proposed model consists of a densely connected network to extract features from ultrasound images. Several layers are added to perform the diagnosis process based on the features extracted by Densnet121. The optimal hyper -parameters for learning rate, batch size, dropout ratio, and number of neurons were found using an optimization algorithm. The improved hippopotamus algorithm (IHOA) is efficient in finding hyper-parameters. The IHOA algorithm is robust in exploring and exploiting solutions to find optimal values, and it does not require a large number of iterations. The dataset used in this paper is AUITD. The number of images used in the paper was 2,121, divided into 1,697 training images and 424 test images. The proposed model achieved an accuracy of 97.7%, precision of 96.3%, recall of 98%, and F1 score of 97.4%.