A Transfer Learning Approach For Deep Learning Based Brain Tumor Segmentation (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Magnetic resonance imaging (MRI) is amongst the prevalent and widely used medical imaging techniques in visualizing and observing the internal organs of the human body. MRI provides a detailed analysis and guidance towards patient’s health condition including the detection of anomalies such as brain tumor. Since manual analysis and detection of tumor is highly time-consuming process and providing medical assistance in critical cases is a biggest challenge for the medical practitioners. An accurate, efficient and advanced computational method is heavily in need for most deadly diseases like brain tumor. This proposed work addresses deep learning coupled with small kernels and handles the obstacles in brain tumor segmentation techniques. This research work is incorporating all the dimensions available as MRI images of brain, constituted and analyzed using convolutional neural network (CNN). It needs high level of computational capacity. The work presented in this paper addresses these concerns using deep learning coupled with small kernels. The model presented in this paper is effectively trained over 150 images in the dataset. The proposed work has attained comparative better results with respect to the dice score coefficient such as (0.78, 0.74, 0.74) for whole tumor, core tumor and active tumor respectively.  .