Journal ArticleOpen Access
TimeDistributed-CNN-LSTM: A Hybrid Approach Combining CNN and LSTM to Classify Brain Tumor on 3D MRI Scans Performing Ablation Study
Author Affiliations
Daffodil International University, Charles Darwin University, University of Louisiana at Lafayette
Published InIEEE Access
Year2022
Citations163
Abstract
Identification of brain tumors and accurate grading at an early stage are crucial in cancer diagnosis, as a timely diagnosis can increase the chances of survival. Considering the challenges and risks of tumor biopsies, noninvasive imaging procedures such as Magnetic Resonance Imaging (MRI) are extensively used in analyzing brain tumors. Recent advances in the field of medical imaging with deep learning using three dimensional (3D) MRI is aiding the clinical experts significantly in the diagnosis of brain tumor. In this study, three BraTS MRI datasets named BraTS 2018, BraTS 2019 and BraTS 2020 are employed to classify brain tumor into high-grade glioma (HGG) and low-grade glioma (LGG) where each of the datasets contains four different sequences of 3D MRI brain…
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