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@article{153950,
        author = {Abhiral Dubey and Atharva Pangerkar and Tejas Pawar and Santhosh Pillai and R.S. Bhoyar},
        title = {Brain Tumor Detection: Model and Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {9},
        pages = {258-264},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153950},
        abstract = {There is a high need of machine learning techniques in the medical field. Many medical applications work on the deep learning and machine learning algorithms. In this project we will going to detect the tumor of an MRI image. Detected output region will provide the information about the patient is having a tumor or not. Currently and before the standard process of detecting the tumor is based on the basic doctor check-up or examination, but this process has many drawbacks as it is not applicable in the crucial time when there are lots of MRI reports. It will be quite hectic for the medical staff. Therefore, we have come up with a solution with this project. The project will be based on the two steps first the detection of the MRI image. With the help of this techniques, we will be able to locate the tumor very effectively. The second step is of classification. Tumor classification purpose we will going to use three classifiers in order to make our system and program very efficient, at the output of this classifiers there won't be any doubt regarding the classification of the MRI image. Here the detection will be going to detect the tumor with various image pre-processing techniques. And for the classification we are using SVM, logistic regression, CNN classifiers in order to classify the MRI images. The principal focus will be to learn program using three classifiers and identifying which one is most efficient one.},
        keywords = {Image detection, Image classification, Machine learning, Image pre-processing techniques, Classifiers (SVM, LG, CNN), Python, Modules or Libraries.},
        month = {},
        }