Advancement in Brain Tumour Detection using Deep Learning Technique

Meena R.*, Sharmila S.**, Sheela E.***, Thamizharasi K.****
*-**** Department of Biomedical Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India.
Periodicity:April - June'2024
DOI : https://doi.org/10.26634/jip.11.2.20915

Abstract

The advancements in medical imaging technology, such as integrating InceptionV3 algorithms with MRI scans, have revolutionized brain tumor detection. These algorithms leverage deep learning to analyze MRI images rapidly and accurately, aiding in the precise identification of potential tumors. This integration enhances the efficiency of radiologists, enabling timely interventions and improving patient outcomes. The seamless synergy between MRI technology an deep learning algorithms marks a significant leap forward in neurology, promising more personalized and effective care for patients with brain tumors. Ongoing innovation in medical imaging and AI holds great potential for further improving diagnostic accuracy and treatment effectiveness in the future.

Keywords

AI Integration, Invasive Surgical Techniques, Early Intervention, InceptionV3, MRI Technology, Brain Tumor Detection, Deep Learning.

How to Cite this Article?

Meena, R., Sharmila, S. Sheela, E., and Thamizharasi, K. (2024). Advancement in Brain Tumour Detection using Deep Learning Technique. i-manager’s Journal on Image Processing, 11(2), 35-39. https://doi.org/10.26634/jip.11.2.20915

References

[8]. Pravitasari, A. P., Iriawan, N., Safa, M. A. I., Fithriasari, K., Purnami, S. W., & Ferriastuti, W. (2019a). MRI-Based brain tumor segmentation using modified stable student's t from burr mixture model with bayesian approach. Malaysian Journal of Mathematical Sciences, 13(3), 297-310.
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