Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging

Abstract

Objective. The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered. This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches. Methods. The training and validation sets comprised randomly selected thyroid and breast nodule images. The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method). A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines. Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves. Results. Among the 4 models, the CNN model using segmented images for classification achieved the best result. For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively. There was no statistically significant difference between the CNN model and the ultrasound guidelines. The combination of the two methods achieved superior diagnostic efficiency compared with their use individually. Conclusions. The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs. The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model. Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems.

Artificial Intelligence in Thyroid Field—A Comprehensive Review

“…In fact, to this date, several studies [ 7 , 20 , 28 , 43 ] recommend that the lesion evaluation should be achieved from a combination between the clinician evaluation and ML or DL outcome. Moreover, it is worth noticing that most AI-based studies focused on thyroid pathologies are performed using retrospectively collected data [ 9 , 11 , 33 , 40 , 42 , 51 , 55 , 60 , 61 , 62 , 63 , 65 , 66 , 67 ]. Conversely, studies that prospectively evaluate AI predictive models concerning thyroid disease diagnosis are limited in the literature [ 22 , 41 ].…”

“…Mainly, AI algorithms have been implemented for the classification of thyroid nodules, i.e., differentiating among benign or malignant state [ 9 , 10 , 21 , 22 , 33 , 41 , 51 , 52 , 53 , 54 , 55 , 56 ]. The outcomes of these studies are compared with the diagnosis of radiologists with different levels of experience.…”

“…Analysis of image features in the context of medical imaging is an emerging field of study but extensive literature already exists [ 7 , 8 , 9 ]. In the majority of earlier works, the image features are analyzed with the aim of detection and diagnosis of abnormal regions within human tissues [ 10 , 11 , 12 ].…”

Multi‐Features‐Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

“…Due to the complexity involved during the ultrasound examination, the acquired images contain speckle noise, image artifacts, and weak boundaries that hurt the segmentation process. Accurate segmentation can effectively improve the accuracy of classification [ 12 ]. Therefore, for accurate extraction of tumor regions, conventional segmentation techniques may not provide desired results.…”

Multilevel Strip Pooling-Based Convolutional Neural Network for the Classification of Carotid Plaque Echogenicity

“…A deep convolutional neural network was trained using 129,450 clinical images of skin disease to classify skin lesions in [ 26 ]. A multiorgan CAD system based on CNNs was developed for classifying both thyroid and breast nodules and investigating the impact of this system on the diagnostic efficiency of different preprocessing approaches [ 27 ]. A deep residual network was applied to automatically extract features of carotid ultrasound images and identify the carotid plaques in the images [ 28 ].…”