Authors :
Anand Ratnakar; Nivea Chougule
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/7crnhrc8
Scribd :
https://tinyurl.com/3rmybphs
DOI :
https://doi.org/10.5281/zenodo.10242815
Abstract :
Brain tumor classification is a critical facet of
medical diagnostics, influencing treatment decisions and
patient outcomes. Traditional diagnostic methods often
rely on manual interpretation of medical images, leading
to challenges in accuracy and efficiency. This project
introduces a revolutionary approach to brain tumor
classification through the implementation of Convolutional
Neural Networks (CNNs). The integration of CNNs, a
subset of deep learning techniques, aims to enhance the
accuracy, speed, and automation of brain tumor
classification, marking a significant leap forward in
medical image analysis.
Brain tumors, both benign and malignant, present
intricate challenges in terms of diagnosis and treatment
planning. Existing diagnostic methods, while valuable, are
often time-consuming and susceptible to interpretative
variations. The motivation behind this project stems from
the need for more robust, automated, and accurate
diagnostic tools. By harnessing the power of CNNs, which
have demonstrated remarkable success in image
recognition tasks, we aim to address the limitations of
traditional diagnostic approaches.
The primary objective of this project is to develop a
CNN-based model capable of accurately classifying brain
tumors from medical images. This encompasses the
identification of tumor types, differentiation between
benign and malignant tumors, and providing a reliable
tool for healthcare practitioners to expedite diagnosis and
treatment planning.
Brain tumor classification is a critical facet of
medical diagnostics, influencing treatment decisions and
patient outcomes. Traditional diagnostic methods often
rely on manual interpretation of medical images, leading
to challenges in accuracy and efficiency. This project
introduces a revolutionary approach to brain tumor
classification through the implementation of Convolutional
Neural Networks (CNNs). The integration of CNNs, a
subset of deep learning techniques, aims to enhance the
accuracy, speed, and automation of brain tumor
classification, marking a significant leap forward in
medical image analysis.
Brain tumors, both benign and malignant, present
intricate challenges in terms of diagnosis and treatment
planning. Existing diagnostic methods, while valuable, are
often time-consuming and susceptible to interpretative
variations. The motivation behind this project stems from
the need for more robust, automated, and accurate
diagnostic tools. By harnessing the power of CNNs, which
have demonstrated remarkable success in image
recognition tasks, we aim to address the limitations of
traditional diagnostic approaches.
The primary objective of this project is to develop a
CNN-based model capable of accurately classifying brain
tumors from medical images. This encompasses the
identification of tumor types, differentiation between
benign and malignant tumors, and providing a reliable
tool for healthcare practitioners to expedite diagnosis and
treatment planning.
