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Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Volume 7, 2019 - Issue 3: Special Issue: 2nd and 3rd MICCAI workshop on Deep Learning in Medical Image Analysis
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Original Articles

Exponential cuckoo search algorithm to Radial Basis Neural Network for automatic classification in MRI images

P. SathishDepartment of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India;School of Information Technology & Engineering, VIT University, Vellore, Tamilnadu, IndiaCorrespondence[email protected]
&
N. M. ElangoDepartment of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India;School of Information Technology & Engineering, VIT University, Vellore, Tamilnadu, India
Pages 273-285 | Received 26 Apr 2017, Accepted 26 Sep 2017, Published online: 22 Nov 2017

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R. Meena Prakash, R. Shantha Selva Kumari, K. Valarmathi & K. Ramalakshmi. (2023) Classification of brain tumours from MR images with an enhanced deep learning approach using densely connected convolutional network. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 11:2, pages 266-277.
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Hanene Sahli, Mounir Sayadi & Radhouane Rachdi. (2020) Intelligent detection of fetal hydrocephalus. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 8:6, pages 641-648.
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Articles from other publishers (3)

O.V. Barabash, O.V. Svynchuk, O.I. Bandurka & P.V. Open’ko. (2022) INFORMATION SYSTEM FOR SELECTION OF OPTIMAL STRATEGIES FOR TREATMENT OF VIRAL DISEASES. Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University:75, pages 41-50.
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Deepak O. Patil & Satish T. Hamde. (2021) Automated detection of brain tumor disease using empirical wavelet transform based LBP variants and ant-lion optimization. Multimedia Tools and Applications 80:12, pages 17955-17982.
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Deepak O. Patil & Satish T. Hamde. (2019) Brain MR Imaging Tumor Detection Using Monogenic Signal Analysis-Based Invariant Texture Descriptors. Arabian Journal for Science and Engineering 44:11, pages 9143-9158.
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