Evaluation of the performance of traditional machine learning algorithms, convolutional neural network and AutoML Vision in ultrasound breast lesions classification: a comparative study

Abstract

Background: In recent years, there was an increasing popularity in applying artificial intelligence in the medical field from computer-aided diagnosis (CAD) to patient prognosis prediction. Given the fact that not all healthcare professionals have the required expertise to develop a CAD system, the aim of this study was to investigate the feasibility of using AutoML Vision, a highly automatic machine learning model, for future clinical applications by comparing AutoML Vision with some commonly used CAD algorithms in the differentiation of benign and malignant breast lesions on ultrasound. Methods: A total of 895 breast ultrasound images were obtained from the two online open-access ultrasound breast images datasets. Traditional machine learning models (comprising of seven commonly used CAD algorithms) with three content-based radiomic features (Hu Moments, Color Histogram, Haralick Texture) extracted, and a convolutional neural network (CNN) model were built using python language.AutoML Vision was trained in Google Cloud Platform. Sensitivity, specificity, F1 score and average precision (AUCPR) were used to evaluate the diagnostic performance of the models. Cochran's Q test was used to evaluate the statistical significance between all studied models and McNemar test was used as the post-hoc test to perform pairwise comparisons. The proposed AutoML model was also compared with the current related studies that involve similar medical imaging modalities in characterizing benign or malignant breast lesions.

Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence

“…These benefits were recently described in a study that compared the performance of Google AutoMl with CNN and traditional machine learning models comprised of seven computer-aided diagnosis algorithms written in, and interestingly found out that the Google AutoML performance was not significantly different from neither CNN nor the best classifier of computer-aided diagnosis algorithms to diagnose breast cancer. 34 While encouraging, this study had several limitations. The study employed data collection from a retrospective cohort and while efforts were made to select images in the same plane, our dataset could not be standardized in terms of image axis, plane, and view.…”

Rapid and automatic assessment of early gestational age using computer vision and biometric measurements based on ultrasound video

“…This study also provided 159 video datasets and we have made them publicly available (https://drive.google.com/ drive/folders/1cP25UNROveiafvumT9vInQ2OQj3xdug?usp=sharing) so that more researchers can participate in improving and clinically verifying this method. In the future, we hope to expand and improve this computing framework through deep learning, artificial intelligence, and image segmentation (27)(28)(29).…”

RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

“…On the other hand, the proposed method using Database-II gives an accuracy of 98.35% with an F1 score of 0.984, which is significantly better. The method of Ka Wing Wan et al [43] provides accuracies of 91%, with an F1 score of 0.87 with a CNN, and 90%, with an F1 score of 0.83 using a Random Forest classifier for the Database-III. Moon et al [44] reported an accuracy of 94.62% with an F1 score of 0.911, using the same Database-III.…”

“…In contrast, the accuracy and F1 score for the proposed method are superior. Furthermore, the proposed CNN-based approach is applied for classification on the Database-III with 80% training and 20% testing ratio with the same validation approach as in [43,44]. This experiment provides an accuracy of 96.45%, a sensitivity 93.09%, a specificity 98.14% with an F1 score of 0.946, still superior to those of [43,44].…”