Utilizing Collaborating Biomedical Deep Model for Diagnosis of Ultrasonography Tumor Images

Authors

Maytham N. Meqdad, Intelligent Medical Systems Department, College of Sciences, Al-Mustaqbal University, Hillah 51001, IraqFollow
Seifedine Kadry, Department of Applied Data Science, Noroff University College, Kristiansand; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates; and Department of Electrical and Computer Engineering, Lebanese American University, Byblos, LebanonFollow

Document Type

Original Study

Abstract

Artificial intelligence models are the most common methods of diagnosing various diseases today. In the meantime, the use of deep learning for faster diagnosis with less error, which is usually less expensive than other methods, is noteworthy. However, these methods are expensive due to the problems of accessing data, including care data such as hospitals and clinics. This article presents a novel federated learning-based approach for ultrasound mammography image classification. In this method, the clients possess their own datasets and are trained to use them. Then, the server, with the help of the trained clients, obtains the best parameters for its model. The most important innovation of this article is the use of a shared biomedical model in which the internal features of the models are shared among each other. The proposed method has an average of 9.99% higher accuracy compared to similar studies.

Recommended Citation

Meqdad, Maytham N. and Kadry, Seifedine (2024) "Utilizing Collaborating Biomedical Deep Model for Diagnosis of Ultrasonography Tumor Images," Al-Mustaqbal Journal of Sustainability in Engineering Sciences: Vol. 2 : Iss. 1 , Article 1.
Available at: https://doi.org/10.62723/2959-5932.1010