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Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation
Author(s) -
Iqbal Sajid,
Ghani Khan Muhammad U.,
Saba Tanzila,
Mehmood Zahid,
Javaid Nadeem,
Rehman Amjad,
Abbasi Rashid
Publication year - 2019
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.23281
Subject(s) - artificial intelligence , computer science , convolutional neural network , pattern recognition (psychology) , segmentation , deep learning , data set , preprocessor , benchmark (surveying) , artificial neural network , noise (video) , weighting , machine learning , image (mathematics) , geodesy , geography , medicine , radiology
Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.