Brain tumor identification and classification using machine learning
By: Nakul, R. S
.
Contributor(s): Gunavantha, P.
Publisher: Gurugram IOSR - International Organization of Scientific Research 2022Edition: Vol.24(5), Sep-Oct.Description: 34-37p.Subject(s): Computer EngineeringOnline resources: Click here
In:
IOSR Journal of Computer Engineering (IOSR-JCE)Summary: Brain Cancer (Tumor) can be characterized as an abnormal and unrestrained development in the
synapses. Research has shown that the identification of these tumors at an early stage can help deal with the
severity in a significant manner. Taking into the account the number of undetected brain tumors, there is a
strong need for improvement in this field of identifying and understanding the formation of the tumor. This not
only helps in detection of plausible problem in patients, but also helps the doctors, by increasing the ease and
time take for the same. The main motive of the paper is to find a generalized technique to detect brain tumors
more efficiently and classify the images
based on the stages of growth. Using these images we can establish growth parameters like the type of tumor,
severity of growth and treatment elimination. Doctors can use these data to eliminate certain unnecessary
procedures which can save a lot of lives. The paper uses Machine Learning through Tensor Flow and Keras to
train efficient models for detection, localization and classification of brain tumors using predefined dataset.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
School of Engineering & Technology Archieval Section | Not for loan | 2023-0081 |
Brain Cancer (Tumor) can be characterized as an abnormal and unrestrained development in the
synapses. Research has shown that the identification of these tumors at an early stage can help deal with the
severity in a significant manner. Taking into the account the number of undetected brain tumors, there is a
strong need for improvement in this field of identifying and understanding the formation of the tumor. This not
only helps in detection of plausible problem in patients, but also helps the doctors, by increasing the ease and
time take for the same. The main motive of the paper is to find a generalized technique to detect brain tumors
more efficiently and classify the images
based on the stages of growth. Using these images we can establish growth parameters like the type of tumor,
severity of growth and treatment elimination. Doctors can use these data to eliminate certain unnecessary
procedures which can save a lot of lives. The paper uses Machine Learning through Tensor Flow and Keras to
train efficient models for detection, localization and classification of brain tumors using predefined dataset.
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