TY  - JOUR
Y1  - 2022///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124069524&doi=10.1007%2f978-3-030-90618-4_25&partnerID=40&md5=39057fa774f9c7ce7fea4b623f7dd416
A1  - Rafi, A.
A1  - Khan, Z.
A1  - Aslam, F.
A1  - Jawed, S.
A1  - Shafique, A.
A1  - Ali, H.
JF  - Lecture Notes on Data Engineering and Communications Technologies
VL  - 105
N2  - Medical imaging techniques are a vital tool in disease diagnosis. The images are being developed to satisfy the growing need for important information from medical image scans by anticipating constitutional tissues for clinical analysis. The application of deep learning techniques is increasing with the demand for automatic diagnosis of medical imaging. Different layers are used in deep learning models to represent data abstraction and construct computational models. Imaging techniques allow medical experts such as radiologists to correctly recognize a patientâ??s condition, making medical procedures more accessible and automated. The reviewâ??s primary goal is to present a study on recent brain tumor detection segmentation and classification approaches. Brain tumors are reviewed because of their importance compared to other tumors and their high illness rate. Many brain tumor segmentation models have been described to grasp these methodologies well, along with their limits and benefits. The study focuses primarily on contemporary deep learning-based brain tumor detection technologies, such as deep generative and deep learning networks. The more advanced and recent techniques available in the literature are also reviewed to describe the methods for performing image segmentation and to emphasize the importance of segmentation models that are not used in real-time due to little or no interaction between clinicians and developers. Most research does not consider the data augmentation element of brain tumor segmentation, which is critical for improving performance. The most challenging feature, or limitation, is the fluctuation in the morphology of tumors or the intensity degree of tumors, both of which still require study in this arena. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
KW  - Deep learning; Diagnosis; Image segmentation; Magnetic resonance imaging; Medical imaging; Tumors
KW  -  Automatic algorithms; Brain tumor segmentation; Brain tumors; Deep generative network; Deep learning network; Disease diagnosis; Learning network; Medical imaging techniques; Segmentation models; Tumour detection
KW  -  Brain
ID  - scholars17765
SN  - 23674512
PB  - Springer Science and Business Media Deutschland GmbH
EP  - 522
AV  - none
N1  - cited By 3
SP  - 505
TI  - A Review: Recent Automatic Algorithms for the Segmentation of Brain Tumor MRI
ER  -