Show simple item record

AuthorHossain, Amran
AuthorIslam, Mohammad T.
AuthorRahman, Tawsifur
AuthorChowdhury, Muhammad E. H.
AuthorTahir, Anas
AuthorKiranyaz, Serkan
AuthorMat, Kamarulzaman
AuthorBeng, Gan K.
AuthorSoliman, Mohamed S.
Available date2023-09-24T08:57:19Z
Publication Date2023
Publication NameBiosensors
ResourceScopus
URIhttp://dx.doi.org/10.3390/bios13030302
URIhttp://hdl.handle.net/10576/47897
AbstractAutomated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation of tumors are extremely time-consuming but crucial tasks due to the tumor's pattern. In this paper, we propose a new lightweight segmentation model called MicrowaveSegNet (MSegNet), which segments the brain tumor, and a new classifier called the BrainImageNet (BINet) model to classify the RMW images. Initially, three hundred (300) RMW brain image samples were obtained from our sensors-based microwave brain imaging (SMBI) system to create an original dataset. Then, image preprocessing and augmentation techniques were applied to make 6000 training images per fold for a 5-fold cross-validation. Later, the MSegNet and BINet were compared to state-of-the-art segmentation and classification models to verify their performance. The MSegNet has achieved an Intersection-over-Union (IoU) and Dice score of 86.92% and 93.10%, respectively, for tumor segmentation. The BINet has achieved an accuracy, precision, recall, F1-score, and specificity of 89.33%, 88.74%, 88.67%, 88.61%, and 94.33%, respectively, for three-class classification using raw RMW images, whereas it achieved 98.33%, 98.35%, 98.33%, 98.33%, and 99.17%, respectively, for segmented RMW images. Therefore, the proposed cascaded model can be used in the SMBI system.
SponsorThis work was supported by the Universiti Kebangsaan Malaysia (UKM), project grant code: DIP-2020-009. This work was also supported by Grant NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar, and student grant from Qatar University, Grant # QUST-1-CENG-2023-796. The claims made herein are solely the responsibility of the authors. Open access publication is supported by Qatar National Library.
Languageen
PublisherMDPI
Subjectantenna sensor
brain tumor segmentation
classification
deep learning
Self-ONN
sensor-based microwave brain imaging system
TitleBrain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models
TypeArticle
Pagination-
Issue Number3
Volume Number13
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record