An Overall Survey of Brain Tumor Detection with Improved Machine Learning and Deep Learning Techniques


  • Srinivas Kumar Palvadi Research Scholar, Department of Computer Science and Engineering, Pondicherry University, Puducherry, Kalapet - 605014
  • K. Suresh Joseph Associate Professor, Department of Computer Science and Engineering, Pondicherry University, Puducherry, Kalapet - 605014


Segmentation, Feature Extraction, Validation, Disease


Cancer ID plays the crucial role in identifying the type of therapy, treatment progress, success rate, and disease advancement. CNN were the pivotal class at deep learning, particularly in recognizing visual imagery. CNNs train  through convolution & maxpooling layers. ELM were the type for trainind mechanisms with hidden layers, applied at multiple domains like classification & regression.Gliomas, which is the common as well as violent brain cancers, significantly impact patient survival. Therefore, effective treatment planning is vital for enhancing life time for oncological patients. MRI which is very often used for identifying tumor. However, extensive information generated with MRI impedes traditional filtering within proper timeframe, by limits of the application in identifying the quality in terms of medical data. Hence, there is a need for reliable and automated segmentation methods.


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How to Cite

Palvadi, S. K. ., & Joseph, K. S. . (2024). An Overall Survey of Brain Tumor Detection with Improved Machine Learning and Deep Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 425 –. Retrieved from



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