A Review : Brain tumor detection using Digital Image Processing

Authors

  • Dinesh M. Barode  Research Scholar, Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
  • Rupali S. Awhad  Research Scholar, Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
  • Seema S. Kawathekar  Assistant Professor, Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT2390154

Keywords:

DIP (Digital Image Processing), MRI (Magnetic resonance Image), Brain Tumor, classification, Segmentation, Feature Extraction, SVM (Support vector Machine)

Abstract

Now a day, one of the most common diseases is a brain tumor. The challenge is to identify a tumor at an early stage, which is essential to receiving good care and surviving brain cancer patients. In the human body, the uncontrolled growth of cells is called a brain tumor. They have different types and characteristics and have different treatments. Medical imaging techniques play an important role in the detection of brain tumors. Although MRI (Magnetic Resonance Imaging) is frequently regarded as the best method for identifying this type of tumor, it has several drawbacks, and MRI images are more sensitive to ambient noise and other disruptions. As a result, it is challenging for doctors to identify the tumor and its origin.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-04-30

Issue

Volume 9, Issue 2, March-April-2023

Section

Research Articles

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How to Cite

[1]
Dinesh M. Barode, Rupali S. Awhad, Seema S. Kawathekar, " A Review : Brain tumor detection using Digital Image Processing" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.61-65, March-April-2023. Available at doi : https://doi.org/10.32628/CSEIT2390154