Classification of Brain Tumors using Fuzzy C-means and VGG16

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V Ramakrishna Sajja, et. al.

Abstract

Brain tumor is a treacherous and pernicious type of cancer detected in grown-ups and kids.It is critical to pinpoint the primary and precise brain tumors in the recovery process. Abnormal cell development within the skull is a brain tumor. With direct effects such as cognitive impairment and poor quality of life, malignant brain tumors are among the most dreadful forms of cancer. Brain tumors are a lethal cancer, and because of the heterogeneous nature of the tumor cells, their classification is a difficult challenge for radiologists. Stratifying the Brain Tumors (BT)  is a pivotal assignment for tumor diagnosis and proper care. Several imaging techniques are in use to identify tumors in the brain. Because of its unrivaled image clarity and the fact that it does not rely on ionizing radiation, Magnetic Resonance Imaging (MRI) is commonly utilized for such a mission. In medical imaging field, the importance of Artificial Intelligence (AI) in the context of Deep Learning (DL) has paved the way for extraordinary advances in categorizing and predicting intricate pathological diseases, such as brain tumors, etc. Deep learning has proven and shown an amazing presentation, particularly in segmenting brain tumors and classifying them. In this study,AI-based classification of BT using Deep Learning Algorithms for stratifying different brain tumor kinds is suggested using publicly available datasets. These datasets classify(malignant and benign) BTs. The datasets contain 696 images for research purposes on T1-weighted images. The predicted arrangement produces a noteworthy efficiency of 99.04 percent for the highest precision. The result obtained reflects the potential of the proposed algorithm to identify brain tumors.

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