Project Description: A brain tumor is understood by the scientific community as the growth of abnormal cells in the brain, some of which can lead to cancer. The traditional method to detect brain tumors is nuclear magnetic resonance (MRI). Having the MRI images, information about the uncontrolled growth of tissue in the brain is identified.
In this machine learning project, we build a classifier to detect the brain tumor (if any) from the MRI scan images.
Machine Learning (ML) is a study of algorithms and statistical paradigms that can be utilized to perform a specified task without using explicit instructions, depending on patterns instead of those.9 Also, machine learning is the operation of supply computers with they might learn by utilizing the data and experience such as human brain. The major aim of machine learning is to make algorithms which can train themselves to get better, realize complex patterns, and get solutions to the modern problems by utilizing the prior data.10 Machine learning algorithms have widely stood out in the medical imaging field as a part of artificial intelligence.11 It can be split into two major categories, supervised and unsupervised. In supervised techniques, an algorithm is utilized to find the mapping function of input variables and their linked output labels to predict modern subjects’ labels. The essential goal is to learn ingrained patterns within the training data utilizing algorithms such as Artificial Neural Networks ANN12, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN).13 There are two kinds of supervised learning are classification and regression.12 As for, unsupervised learning is based on only the input variables such as fuzzy c-means and others.14 Also, there are two types of unsupervised learning are clustering and association.10 Deep learning is a subset of ML that is based on artificial neural networks with representation learning. These neural networks attempt to simulate the behavior of the human brain— albeit far from matching its ability—allowing it to “learn” from large amounts of data.12 Early detection and diagnosis for brain tumors becomes an important matter in evaluating the patients and helps in choosing the most adequate treatment to save patients’ lives. Sometimes complex cases in the diagnosis stage can be confusing and Sometimes complex cases in boring for doctors. Cases like these require experts to work, identify tumors, compare tumor tissue for the surrounding areas, and apply filters to images, if necessary, to become clearer for human vision, and this task takes time and is prone to human error. Delayed detection of tumors and brain tumors, in particular, are the cause for the death of a large number of patients.
Project Theme: Strengthen the Health Infrastructure