Federated learning for brain tumor segmentation and classification : A statistical approach

Nadarajan, Sivakumar; Unhelkar, Bhuvan; Shankar, S. Siva; Chakrabarti, Tulika; Chakrabarti, Prasun; Sivaneasan, B.; Margala, Martin

doi:10.47974/JSMS-1315

Open Access Research Article

Federated learning for brain tumor segmentation and classification : A statistical approach

^*Sivakumar NadarajanCorresponding authordrsivakumar.nadarajan@gmail.comAffiliation 1Muma College of BusinessFlorida 8350 N. Tamiami Trail SarasotaUniversity of South FloridaFlorida, FL 34243, U.S.A.Affiliation 2Department of Computer Science and Information TechnologyJAIN (Deemed-to-be University)Bengaluru, Karnataka, 560069, IndiaView full profile → , Bhuvan Unhelkarbunhelkar@usf.eduMuma College of Business8350 N. Tamiami Trail SarasotaUniversity of South FloridaFlorida, FL 34243, U.S.A.0000-0003-1118-3837View full profile → , S. Siva Shankardrsivashankars@gmail.comDepartment of Computer Science and EngineeringKG Reddy College of Engineering and TechnologyHyderabad, Telangana, 500075, India0000-0002-7616-6088View full profile → , Tulika Chakrabartitulika.chakrabarti@spsu.ac.inDepartment of ChemistrySir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, IndiaView full profile → , Prasun Chakrabartidrprasun.cse@gmail.comDepartment of Computer Science and EngineeringSir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, India0000-0001-8062-4144View full profile → , B. Sivaneasansivaneasan@singaporetech.edu.sgSpecialist Adult Educator EngineeringElectrical Power Engineering Programme, 1 Punggol Coast RoadSingapore Institute of Technology828608, SingaporeView full profile → , Martin Margalamartin.margala@louisiana.eduSchool of Computing and InformaticsUniversity of Louisiana at LafayetteLA 70503, U.S.A.View full profile →

* Corresponding author · click or hover a name for details

pp. 67–77Vol. 28Issue 1January 2025DOI: 10.47974/JSMS-1315XML

Received:: 13 Feb 2024
Published Online:: 15 Jan 2025
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1315
Pages:: 67–77

Abstract

Enhancing the chances of survival for cancer patients requires early brain tumour identification. This study suggests a federated learning-based method to deal with classification problems caused by overfitting. With the use of the BRATS 2019 and 2020 datasets, the method successfully divides and categorises brain tumours. Data preprocessing involves min-max normalization, followed by CNN segmentation from MRI images. Extracted features undergo shape, texture, and Resnet-50-based feature extraction. These features are then inputted into an LSTM classifier, utilizing Federated Learning for improved classification. Findings indicate that the suggested FL utilizing the CNN-LSTM model outperforms other techniques like U-net and ensemble models, achieving high accuracy: 99.59% on BRATS 2019 and 99.61% on BRATS 2020 datasets.

Keywords

Brain tumor CNN Federated learning (FL)LSTM Magnetic resonance images Statistical approach Applied statistics

Subject Classifications

68T0792C20

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