Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification

TLDR

Image processing, especially MRI, is crucial for diagnosing brain tumors, but manual segmentation is error‑prone and time‑consuming. The study proposes a deep‑learning method to classify brain tumors from MRI data to aid clinicians. The approach preprocesses MRI scans, segments tumors with k‑means clustering, augments data synthetically, and classifies lesions as benign or malignant using a fine‑tuned VGG19 model, evaluated on BraTS 2015. The method achieved higher accuracy than prior state‑of‑the‑art techniques on BraTS 2015.

Abstract

Abstract Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification. Magnetic resonance imaging (MRI) is favored among all modalities due to its noninvasive nature and better representation of internal tumor information. Indeed, early diagnosis may increase the chances of being lifesaving. However, the manual dissection and classification of brain tumors based on MRI is vulnerable to error, time‐consuming, and formidable task. Consequently, this article presents a deep learning approach to classify brain tumors using an MRI data analysis to assist practitioners. The recommended method comprises three main phases: preprocessing, brain tumor segmentation using k‐means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (i.e., 19 layered Visual Geometric Group) model. Moreover, for better classification accuracy, the synthetic data augmentation concept i s introduced to increase available data size for classifier training. The proposed approach was evaluated on BraTS 2015 benchmarks data sets through rigorous experiments. The results endorse the effectiveness of the proposed strategy and it achieved better accuracy compared to the previously reported state of the art techniques.