Lathashree, P.S.Puthran, P.Yashaswini, B.N.Patil, N.2026-02-062022Lecture Notes in Networks and Systems, 2022, Vol.436, , p. 231-24523673370https://doi.org/10.1007/978-981-19-1012-8_16https://idr.nitk.ac.in/handle/123456789/29922The current study focuses on brain tumor segmentation which is a relevant task for processing a medical image. The treatment possibilities can be increased by diagnosing the brain tumor at early stages which also helps in enhancing the survival rates. It is a hard and time-consuming task to perform the processes like brain tumor segmentation from huge number of magnetic resonance imaging (MRI) images created in laboratories manually for cancer diagnosis. Hence, there arises a need for the segmentation of brain images for tumor identification. In this paper, we attempted to address the existing problems and generate the image segmentation using fuzzy C-means (FCM) clustering method and then the classification of brain MR images using ResNet-50 model. A dataset provided by Kaggle comprising brain MR images has been used in training and testing the model. The image segmentation results are compared with the K-means clustering method using various performance metrics. The maximum accuracy of 91.18% has been observed at 350 epochs by the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Brain image segmentationFuzzy C-means clustering methodMRI imagesResNet-50Tumor identification