IMAGE SEGMENTATION FOR BRAIN TUMOR DIAGNOSIS: A POSSIBLE APPLICATION OF MACHINE LEARNING

Digambar Jadhav, Pankaj Kumar Payal Bhargava

Abstract

Image segmentation is essential for the prognosis of brain tumors. Accurate and quick MRI segmentation
is crucial for the diagnosis, treatment, and prognosis of brain tumors. In this study, we provide a machine
learning-based approach to the problem of segmenting brain tumors. Improved outcomes for patients with
brain tumors may be expected as a consequence of the use of cutting-edge deep learning techniques with
massive MRI datasets. Brain tumor segmentation in medical images is particularly challenging. An
accurate method of dividing up brain tumors is needed. When it comes to image categorization, object
identification, and semantic segmentation, deep learning algorithms have excelled. Segmentation methods
for brain tumors based on deep learning show promise. Segmentation of brain tumors using deep learning
is the topic of this essay. A total of 150 academic publications are reviewed, including topics like network
architecture design, imbalanced segmentation, and multi-modality processes. Goal-setting conversations
may be quite illuminating. Brain tumors are the result of unchecked cell proliferation. Brain tumors are
diverse. Brain tumors may be either malignant or benign. This technique uses image segmentation and
classification to automate the identification of brain tumors. Brain tumors can come in a wide range of
sizes. Use deep neural networks with a lot of memory. Our methods are outlined below for your perusal.
Kaggle data was used to evaluate the trained model. There are 5,000 images for segmentation and 8,000
for classification in this set of data. MRI scans may be broken up into patches. On test data, this method
proved to be 95.6% accurate. We tried several configurations of neural network layers to settle on the best
one. A glioma was identified via convolutional neural networks. Spectrum-based lilac cell glioma
localization was achieved by means of deep and convolutional neural networks. Potentially, this
framework can foretell the future.