ArticleJournal of digital imaging2019
AdaptAhead Optimization Algorithm for Learning Deep CNN Applied to MRI Segmentation.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep Learning Algorithm-Based MRI Image in the Diagnosis of Diabetic Macular Edema.Contrast media & molecular imaging · 2022Trial
- Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.Multiple sclerosis (Houndmills, Basingstoke, England) · 2020Trial
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Automatic brain tumor detection using CNN transfer learning approach.Medical & biological engineering & computing · 2023Article
- Improving the efficiency of RMSProp optimizer by utilizing Nestrove in deep learning.Scientific reports · 2023Article
- Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network.Journal of healthcare engineering · 2022Article
- Nonlinear Hyperparameter Optimization of a Neural Network in Image Processing for Micromachines.Micromachines · 2021Article
- Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.Scientific reports · 2021Article
- Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art.Journal of imaging · 2021Review
- Deep Learning-Based Magnetic Resonance Imaging Features in Diagnosis of Perianal Abscess and Fistula Formation.Contrast media & molecular imaging · 2021Article
- Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network.RSC advances · 2020Article
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Authors and funding
3 authors.
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Abstract
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.
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