» Articles » PMID: 33222016

Tumor Type Detection in Brain MR Images of the Deep Model Developed Using Hypercolumn Technique, Attention Modules, and Residual Blocks

Overview
Publisher Springer
Date 2020 Nov 22
PMID 33222016
Citations 16
Authors
Affiliations
Soon will be listed here.
Abstract

Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection.

Citing Articles

A Novel Grammar-Based Approach for Patients' Symptom and Disease Diagnosis Information Dissemination to Maintain Confidentiality and Information Integrity.

Nag S, Basu N, Bose P, Bandyopadhyay S Bioengineering (Basel). 2025; 11(12.

PMID: 39768084 PMC: 11673805. DOI: 10.3390/bioengineering11121265.


Enhancing brain tumor detection in MRI images using YOLO-NeuroBoost model.

Chen A, Lin D, Gao Q Front Neurol. 2024; 15:1445882.

PMID: 39239397 PMC: 11374633. DOI: 10.3389/fneur.2024.1445882.


Automatic detection and visualization of temporomandibular joint effusion with deep neural network.

Lee Y, Jeon S, Won J, Auh Q, Noh Y Sci Rep. 2024; 14(1):18865.

PMID: 39143180 PMC: 11324909. DOI: 10.1038/s41598-024-69848-9.


Advancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics.

Ozdemir C, Dogan Y Med Biol Eng Comput. 2024; 62(7):2165-2176.

PMID: 38483711 PMC: 11190006. DOI: 10.1007/s11517-024-03064-5.


Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.

Pitarch C, Ungan G, Julia-Sape M, Vellido A Cancers (Basel). 2024; 16(2).

PMID: 38254790 PMC: 10814384. DOI: 10.3390/cancers16020300.


References
1.
Lah T, Novak M, Breznik B . Brain malignancies: Glioblastoma and brain metastases. Semin Cancer Biol. 2019; 60:262-273. DOI: 10.1016/j.semcancer.2019.10.010. View

2.
Wang J, Pulido J, Patrick ONeill B, Johnston P . Second malignancies in patients with primary central nervous system lymphoma. Neuro Oncol. 2014; 17(1):129-35. PMC: 4483043. DOI: 10.1093/neuonc/nou105. View

3.
Siegel R, Miller K, Jemal A . Cancer statistics, 2019. CA Cancer J Clin. 2019; 69(1):7-34. DOI: 10.3322/caac.21551. View

4.
Minniti G, Filippi A, Osti M, Ricardi U . Radiation therapy for older patients with brain tumors. Radiat Oncol. 2017; 12(1):101. PMC: 5477302. DOI: 10.1186/s13014-017-0841-9. View

5.
Hanif F, Muzaffar K, Perveen K, Malhi S, Simjee S . Glioblastoma Multiforme: A Review of its Epidemiology and Pathogenesis through Clinical Presentation and Treatment. Asian Pac J Cancer Prev. 2017; 18(1):3-9. PMC: 5563115. DOI: 10.22034/APJCP.2017.18.1.3. View