Journal of Digital Imaging

, Volume 32, Issue 1, pp 105–115 | Cite as

AdaptAhead Optimization Algorithm for Learning Deep CNN Applied to MRI Segmentation

  • Farnaz Hoseini
  • Asadollah ShahbahramiEmail author
  • Peyman Bayat


Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large volumes of data. In fact, in deep learning, the high-level concepts are defined by low-level features. Previously, in optimization algorithms, the accuracy achieved for network training was less and high-cost function. In this regard, in this study, AdaptAhead optimization algorithm was developed for learning DCNN with robust architecture in relation to the high volume data. The proposed optimization algorithm was validated in multi-modality MR images of BRATS 2015 and BRATS 2016 data sets. Comparison of the proposed optimization algorithm with other commonly used methods represents the improvement of the performance of the proposed optimization algorithm on the relatively large dataset. Using the Dice similarity metric, we report accuracy results on the BRATS 2015 and BRATS 2016 brain tumor segmentation challenge dataset. Results showed that our proposed algorithm is significantly more accurate than other methods as a result of its deep and hierarchical extraction.


Deep learning Convolutional neural networks MRI segmentation Deep convolutional neural networks Optimization algorithm 


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Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  • Farnaz Hoseini
    • 1
  • Asadollah Shahbahrami
    • 2
    Email author
  • Peyman Bayat
    • 1
  1. 1.Department of Computer Engineering, Rasht BranchIslamic Azad UniversityRashtIran
  2. 2.Department of Computer Engineering, Faculty of EngineeringUniversity of GuilanRashtIran

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