Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2022.2103771
ERCNN-DRM: an efficient regularized convolutional neural network with a dimensionality reduction module for the classification of brain tumour in magnetic resonance images
Selvin Prem Kumar S
; Department of Computer Science and Engineering, CSI Institute of Technology, Thovalai, Tamil Nadu, India
Agees Kumar C
; Department of EEE, Arunachala College of Engineering for Women, Nagercoil, Tamil Nadu, India
Jemila Rose R
; Department of CSE, St. Xavier’s Catholic College of Engineering, Nagercoil, Tamil Nadu, India
Sažetak
Brain tumour is a severe disease that may lead to death if unrecognized and untreated. Brain tumor identification and segmentation is a complex and task in medical image processing. For radiologists, diagnosing and classifying tumor from various images is a challenging process. When the data size is large, deep learning methods outperform conventional learning algorithms. Convolutional Neural Networks are found to be one of the popular deep learning architectures. We propose a deep network with an Efficient Regularized CNN with Dimensionality Reduction Module (ERCNN-DRM), which works with less training data and produces more precise classification with minimal processing time and regularisation. The images are pre-processed, segmented and then the dimension reduced features are extracted using the proposed algorithms and then the proposed regularized classification takes place. The experiment is conducted on TCIA dataset which contains a total of 696 MRI, 224 of which are benign and 472 of which are malignant. The proposed scheme produces accuracy rate of 96.7% and reduces the complexity by working on dimensional reduced data. Performance measures such as accuracy, recall, precision, F-measures are analysed and the system is found to be significant than other state-of-the art.
Ključne riječi
ERCNN; brain tumour; deep learning; classification; dimensionality reduction; MRI
Hrčak ID:
287948
URI
Datum izdavanja:
7.9.2022.
Posjeta: 630 *