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

Chowdhury, Sabrina; Mukhopadhyay, Snehasis; Narendra, Kumpati S.

doi:10.15439/2023F378

Artykuł - szczegóły

Tytuł artykułu

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

Autorzy

Chowdhury Sabrina , Mukhopadhyay Snehasis , Narendra Kumpati S.

Wybrane pełne teksty z tego czasopisma

http://annals-csis.org

Identyfikatory

DOI

10.15439/2023F378

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

mutual learning teacher-student network CNN model distillation kidney disease cyst tumor stone

wzajemna nauka sieć nauczyciel-uczeń CNN choroba nerek torbiel nowotwór

Wydawca

Polskie Towarzystwo Informatyczne

Czasopismo

Annals of Computer Science and Information Systems

Rocznik

2023

Tom

Vol. 35

Strony

401--410

Opis fizyczny

Bibliogr. 47 poz., il., wykr., tab.

Twórcy

autor

Chowdhury Sabrina

sabchow@iupui.edu

Indiana University-Purdue University Computer and Information Science IN 46202, Indianapolis, USA

https://orcid.org/0009-0005-5854-8161

autor

Mukhopadhyay Snehasis

smukhopa@iupui.edu

Indiana University-Purdue University Computer and Information Science IN 46202, Indianapolis, USA

https://orcid.org/0009-0000-0836-2901

autor

Narendra Kumpati S.

kumpati.narendra@yale.edu

Yale University Center for Systems Science CT 06520, New Haven, USA

Bibliografia

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Uwagi

1. The research reported here was supported by the National Science Foundation under grant numbers 1930601 (to Yale) and 1930606 (to IUPUI).

2. Thematic Tracks Regular Papers

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-c75418b1-7829-4567-b92d-60aabbb269fc