Deep learning techniques for efficient brain cancer identification
By: K. S. Mithra
References
- A
ggarwal , M., Tiwari , A. K., Sarathi , M.et al . (2023) An early detection and segmentation of Brain Tumor using Deep Neural Network. BMC Med. Inform. Decis. Mak. 23, 78 https://doi.org/10.1186/s12911-023-02174-8 - A
hmad , M. A. B. (2013) Mining Health Data for Breast Cancer Diagnosis Using Machine Learning. Canberra, ACT, Australia: Univ. of Canberra. - A
l -Issa , Y., Ottom , M. A.and Tamrawi , A. (2019) eHealth cloud security challenges: A survey. J. Healthcare Eng., 2019, 1–15, Sep. 2019. - A
nantharajan , S., Gunasekaran , S., Subramanian , T.and Venkatesh , R. (2024) MRI brain tumor detection using deep learning and machine learning approaches. Measurement: Sensors, 31, 101026. DOI: https://doi.org/10.1016/j.measen.2024.101026 - C
olby , J. B., Soderberg , L., Lebel , C., Dinov , I. D., Thompson , P. M.and Sowell , E. R. (2012) Along-tract statistics allow for enhanced tractography analysis. NeuroImage, 59, 4, 3227–3242, Feb. 2012. - D
inov , D. (2018) Data Science and Predictive Analytics. Springer, Cham, Switzerland. - D
inov , D.and Velev , M. V. (2021) Data Science: Time Complexity, Inferential Uncertainty, and Spacetime Analytics. Walter de Gruyter, Berlin, Germany. - D
oi , K. (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imag. Graph. 31(4–5): 198–211. - H
and , D. J., Christen , P.and Kirielle , N. (2021) F*: an interpretable transformation of the F-measure. Mach. Learn. 110, 451–456, DOI: https://doi.org/10.1007/s10994-021-05964 - K
aggle (no date) Brain MRI Images for Brain Tumor Detection Dataset. Available at: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection - K
apur , T., Egger , J., Jayender , J., Toews , M.and Wells , W. M. (2014) Registration and segmentation for image-guided therapy. In: Intraoperative Imaging and Image-Guided Therapy. Springer, New York, NY, USA, 79–91. - L
itjens , G., Kooi , T., Bejnordi , B. E., Setio , A. A. A., Ciompi , F., Ghafoorian , M.,van der Laak , J. A., Van Ginneken , B.and Sánchez , C. I. (2017) A survey on deep learning in medical image analysis. Med. Imag. Anal. 42: 60–88. - L
iu , Z., Tong , L., Chen , L.et al . (2023) Deep learning based brain tumor segmentation: a survey. Complex Intell. Syst. 9, 1001–1026. https://doi.org/10.1007/s40747-022-00815-5 - M
adhupriya , G., Guru , N. M., Praveen , S.and Nivetha , B. (2019) Brain Tumor Segmentation with Deep Learning Technique. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, 758-763, doi: 10.1109/ICOEI.2019.886 2575. - M
andal , P. K., Mahajan , R.and Dinov , I. D. (2012) Structural brain atlases: Design, rationale, and applications in normal and pathological cohorts. J. Alzheimer’s Disease, 31, s3, S169–S188, Sep. 2012. - O
ttom , M. A. (2019) Convolutional neural network for diagnosing Brain tumor cancer. Int. J. Adv. Comput. Sci. Appl., 10, 7, 333–338. - O
ttom , M. A.and Nahar , K. M. (2019) Computer aided diagnosis for chronic kidney disease using data mining. Int. J. Cyber-Secur. Digit. Forensics, 8, 3, 188–194. - P
rasad , R., Saxena , A. K.and Laha , S. (2024) Prediction of brain cancer occurrence and risk assessment of brain hemorrhage using hybrid deep learning technique. Cancer Investigation, DOI: https://doi.org/10.1080/07357907.2024.2431829 - S
un , L., Zhang , S., Chen , H.and Luo , L. (2019) Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning. Front. Neurosci. 13: 810. doi: 10.3389/fnins.2019.00810 - T
ang , Y.et al . (2010) The construction of a Chinese MRI brain atlas: A morphometric comparison study between Chinese and Caucasian cohorts. NeuroImage, 51, 1, 33–41, May 2010. - T
aylor , R. H., Menciassi , A., Fichtinger , G., Fiorini , P.and Dario , P. (2016) Medical robotics and computer-integrated surgery. In: Springer Handbook of Robotics, Springer, 1657–1684.
DOI: https://doi.org/10.2478/candc-2026-0009 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702 (formerly 1314-4081)
Language: English
Page range: 275 - 294
Submitted on: Nov 1, 2024
Accepted on: Mar 1, 2026
Published online: Oct 31, 2026
Published in print: Jun 1, 2026
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2026 K. S. Mithra, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.