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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Mutual Learning Algorithm for Kidney Cyst, Kidney Tumor and Kidney Stone Diagnosis

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DOI: http://dx.doi.org/10.15439/2023F378

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 401–410 ()

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Abstract. Mutual learning is a machine learning algorithm where multiple machine learning algorithms share knowledge among themselves to improve themselves. The utilization of mutual learning algorithms can effectively enhance the efficiency of machine learning and neural networks within a multi-agent system. This approach is particularly useful in scenarios where the system cannot be adequately trained with a large dataset. By exchanging data in a dynamic teacher-student network system, mutual learning can result in efficient learning outcomes. Typically, a large network serves as a static teacher and transfers data to smaller networks, referred to as student networks, to improve their efficiency. In this study, we aim to demonstrate that two small networks can dynamically alternate between the roles of teacher and student to share knowledge, resulting in improved efficiency for both networks. To exemplify this concept, we apply a mutual learning algorithm using convolutional neural networks (CNNs) and Support Vector Machine (SVM) to accurately identify the kidney diseases -- cyst, tumor and stone using image classification algorithm.

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