Research Article
Brain Tumor Classification Through MR Imaging: A Comparative Analysis
@INPROCEEDINGS{10.1007/978-3-031-48888-7_38, author={G . Prasanna Kumar and K. Kiran and Kanakaraju Penmetsa and K . Indira Priyadarsini and Prudhvi Raj Budumuru and Yallapu Srinivas}, title={Brain Tumor Classification Through MR Imaging: A Comparative Analysis}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={Brain tumor Diagnosis; Illness Medical images Classification}, doi={10.1007/978-3-031-48888-7_38} }- G . Prasanna Kumar
K. Kiran
Kanakaraju Penmetsa
K . Indira Priyadarsini
Prudhvi Raj Budumuru
Yallapu Srinivas
Year: 2024
Brain Tumor Classification Through MR Imaging: A Comparative Analysis
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_38
Abstract
Tumor in brain is one of the serious diseases throughout the world and it leads to death around 300 thousand people in 2020. Hence, Brain tumor diagnosis is a sensible and important task in clinical and medical field. Identification of illness, area, depth and severity of the disease are major challenges encountered before technological improvement in the clinical field. These major challenges are fulfilled few decades ago by acquiring images of human body parts with collaborations of electronic and mechanical devices. The familiar medical images are Magnetic Resonance Imaging (MRI) Scan, Computed Tomography (CT) Scan and Positron Emission Tomography (PET) Scan. Manual observation of aforementioned scans may lead error in the treatment. Hence, various image processing algorithms and pre-trained methods have been employed on medical images to identify the accurate location, area, depth and severity of the disease, which effectively improvise the treatment. The evolution process has several stages such as: preprocessing; segmentation; future extraction; and classification. Therefore, this work presents a detailed report of CNN based brain tumor classification methods through MR imaging scans. Finally, the performance measures of brain tumor classification methods have been presented and compared.
