Abstract
Brain tumour segmentation evolved as the dominant task in brain image processing. Most of the contemporary research proposals devise deep neural networks and sparse representation to address this issue. These methods inherently suffer from high computational cost and additional memory requirements. Thus, optimization of the computational cost became a challenging task for the contemporary research. This paper discusses an optimized U-Net model with post-processing for fast brain tumour segmentation. The proposed model includes two phases: training and testing. Training phase computes weights for optimized U-Net and an adaptive threshold value. In the testing phase, a trained U-Net model predicts a rough tumour segment. Adaptive thresholding grabs the final tumour with improved segmentation results. We have considered a brain tumour dataset of 3064 images with three types of brain tumours for evaluation. Our proposed model exhibits superior results than the existing models in terms of recall and dice similarity metrics. It exhibits competitive performance in accuracy and precision. Moreover, the proposed model outperforms its competitive models in training time.
Highlights
Brain cancer is the 10th leading type of cancer that causes death and established as the deadliest hazards in the world [1]
Brain tumour segmentation evolved as the dominant task in brain image processing
Binary images of predicted and ground truth images are considered for the comparison of brain tumour segmentation
Summary
Brain cancer is the 10th leading type of cancer that causes death and established as the deadliest hazards in the world [1]. A brain cancer diagnosis is a tedious task for the neurologists. Neuroscience is enriched with magnetic resonance (MR) imaging. It simplifies the task of brain tumour diagnosis with the visualization of brain structure. According to the American Cancer Society annual report, there is a rapid increase in the number of brain cancer cases [1]. There is a need for automated diagnosis systems to help neurologists. The main objectives of brain tumour diagnosis automation systems are to reduce human intervention and early-stage tumour detection. Some of the contemporary diagnosis applications include brain tumour growth estimation [3], brain health tissue estimation [4], brain tumour nuclei/cell detection [5] and brain image classification [6]
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