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AuthorHossain, Amran
AuthorIslam, Mohammad T.
AuthorAbdul Rahim, Sharul K.
AuthorRahman, Md A.
AuthorRahman, Tawsifur
AuthorArshad, Haslina
AuthorKhandakar, Amit
AuthorAyari, Mohamed A.
AuthorChowdhury, Muhammad E. H.
Available date2023-04-17T06:57:40Z
Publication Date2023
Publication NameBiosensors
ResourceScopus
URIhttp://dx.doi.org/10.3390/bios13020238
URIhttp://hdl.handle.net/10576/41926
AbstractComputerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural network (Self-ONN) is proposed to classify the reconstructed microwave brain (RMB) images into six classes. Initially, an experimental antenna sensor-based microwave brain imaging (SMBI) system was implemented, and RMB images were collected to create an image dataset. It consists of a total of 1320 images: 300 images for the non-tumor, 215 images for each single malignant and benign tumor, 200 images for each double benign tumor and double malignant tumor, and 190 images for the single benign and single malignant tumor classes. Then, image resizing and normalization techniques were used for image preprocessing. Thereafter, augmentation techniques were applied to the dataset to make 13,200 training images per fold for 5-fold cross-validation. The MBINet model was trained and achieved accuracy, precision, recall, F1-score, and specificity of 96.97%, 96.93%, 96.85%, 96.83%, and 97.95%, respectively, for six-class classification using original RMB images. The MBINet model was compared with four Self-ONNs, two vanilla CNNs, ResNet50, ResNet101, and DenseNet201 pre-trained models, and showed better classification outcomes (almost 98%). Therefore, the MBINet model can be used for reliably classifying the tumor(s) using RMB images in the SMBI system. 2023 by the authors.
SponsorThis work was supported by the Universiti Kebangsaan Malaysia project grant code DIP-2021-024. This work was also supported by Grant NPRP12S-0227-190164 from the Qatar National Research Fund, a member of the Qatar Foundation, Doha, Qatar, and the claims made herein are solely the responsibility of the authors. Open access publication is supported by the Qatar National Library.
Languageen
PublisherMDPI
Subjectbrain tumor classification
deep learning
RMB image dataset
self-ONN
sensor-based microwave brain imaging system
stacked antenna sensor
TitleA Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images
TypeArticle
Issue Number2
Volume Number13
dc.accessType Open Access


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