Automatic brain tumor detection using adaptive region growing with thresholding methods

Kadry Ali Ezzat (Higher Technological Institute)
Lamia Nabil Omran (Higher Technological Institute)
Ahmed Adel Ismail (The Higher Institute of Computer and Information Systems)
Ahmed Ibrahim Bahgat El Seddawy (Arab Academy for Science and Technology and Maritime Transport)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 12, No 4: December 2023

Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.