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The computational aspect of this field also faces challenges. The high resolution and
large data volumes of images require resources for processing. This does not require
infrastructure but can also lead to delays in diagnosis and treatment planning, which is not ideal
for medical professionals. Additionally, the costs associated with maintaining and acquiring
systems pose a dilemma for healthcare organizations and research institutions.
Even when computational challenges are addressed network designs like CNNs have their
limitations. They may struggle to capture all the details in an image and techniques, like pooling
can result in the loss of data.
This research aims to overcome these challenges by focusing on optimizing the analysis of brain
tumor images. We place importance on reducing the burden while still capturing crucial tumor
specific information. To achieve this we introduced a pipeline that ensures reproducible models
by standardizing the data.
Our methodology is based on combining data processing, computer vision and learning
techniques. We have innovatively integrated the YOLOv8 model to enable tumor localization
and prediction in unexamined imaging datasets. Through the technique of data stacking we
create three representations of MRI scans along with their masks. Training a model on these
stacked data promises utilization of resources while ensuring accurate tumor predictions.
In summary this study sheds light on how to enhance the methods of identifying tumors in MRI
scans. It combines data processing, computer vision and deep learning techniques using the
YOLOv8 model as a foundation. The discoveries made here have implications, for advancing
medical image analysis. The goal is to achieve more tumor detection, which can greatly impact
diagnosis and treatment processes.