Abstract
Manually delineating brain tumor sub-locality in MR images is very time consuming, requires experienced annotators and has high inter-rater variability. Accurate unmanned segmentation of brain cancer helps in better detection and diagnosis of the disease. Further it aids in treatment planning and monitoring. With recent computational advances in graphic processing units (GPUs), drastically reducing the computational cost of neural networks, we are motivated to propose a six-layer deep autoencoder for segmenting brain tumors in MR images. The autoencoder is trained using 18\(\times \)18 2D patches extracted from multi-sequence MRI T1,T2,T1c & FLAIR. The model is pre-trained using Rmsprop and fine-tuned by exercising stochastic gradient descent (SGD). The proposed model obtained dice score of 0.88, sensitivity of 0.89 and specificity of 0.86 for the whole tumor region.
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Naveena, C., Manjunath Aradhya, V.N., Poornachandra, S., Rudraswamy, S.B. (2021). Brain Tumor Segmentation in MRI Sequences Using Autoencoders. In: Bhateja, V., Satapathy, S.C., Travieso-González, C.M., Aradhya, V.N.M. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 1407. Springer, Singapore. https://doi.org/10.1007/978-981-16-0171-2_51
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DOI: https://doi.org/10.1007/978-981-16-0171-2_51
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