Authors :
Viswanath Veera Krishna Maddinala; Pemmaraju Vishnu Charan; B.C.S Mounika; Md. Maseed Younus
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/3huzcfnu
Scribd :
https://tinyurl.com/b45ye38t
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR569
Abstract :
Brain tumors are abnormal growth of bulk mass
in the brain which might harmful or harmless, posing a
challenge for evaluation due to the protective skull covering,
the cranium. The prior detection and diagnosis is very
important and decide the survival of the patient, if not
diagnosed and treated earlier the life time of the patient is
exponentially decreased which makes Detecting and
predicting brain tumors efficiently is crucial for timely
intervention. Neuroradiology employs various methods such
as biopsy, radioactive iodine testing, and MRI reports, with
MRI being the most prevalent. However, interpreting MRI
reports demands expertise and time, necessitating a more
efficient approach. Hence, we propose leveraging machine
learning and deep learning algorithms to develop a model
for brain tumor detection such as Convolutional Neural
Network (CNN) for image processing and ML algorithms
which take the parameters of an MRI report and predict
the type of tumor for prediction part. The system is time
efficient and comes in handy for the medical practitioner to
analyze the brain tumor in its early stages and treat it
appropriately before the situation gets out of hand and
increases the lifetime of the patient.
Keywords :
Brain Tumors, Neuroradiology, Machine Learning Algorithms, Deep Learning Algorithms.
Brain tumors are abnormal growth of bulk mass
in the brain which might harmful or harmless, posing a
challenge for evaluation due to the protective skull covering,
the cranium. The prior detection and diagnosis is very
important and decide the survival of the patient, if not
diagnosed and treated earlier the life time of the patient is
exponentially decreased which makes Detecting and
predicting brain tumors efficiently is crucial for timely
intervention. Neuroradiology employs various methods such
as biopsy, radioactive iodine testing, and MRI reports, with
MRI being the most prevalent. However, interpreting MRI
reports demands expertise and time, necessitating a more
efficient approach. Hence, we propose leveraging machine
learning and deep learning algorithms to develop a model
for brain tumor detection such as Convolutional Neural
Network (CNN) for image processing and ML algorithms
which take the parameters of an MRI report and predict
the type of tumor for prediction part. The system is time
efficient and comes in handy for the medical practitioner to
analyze the brain tumor in its early stages and treat it
appropriately before the situation gets out of hand and
increases the lifetime of the patient.
Keywords :
Brain Tumors, Neuroradiology, Machine Learning Algorithms, Deep Learning Algorithms.
