Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection
- URL: http://arxiv.org/abs/2301.02317v1
- Date: Thu, 5 Jan 2023 22:25:28 GMT
- Title: Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection
- Authors: Muyiwa Babayomi, Oluwatosin Atinuke Olagbaju, Abdulrasheed Adedolapo
Kadiri
- Abstract summary: This study proposes a model for the early detection of brain tumours using a combination of convolutional neural networks (CNNs) and extreme gradient boosting (XGBoost)
The proposed model, named C-XGBoost has a lower model complexity compared to purely CNNs, making it easier to train and less prone to overfitting.
It is also better able to handle imbalanced and unstructured data, which are common issues in real-world medical image classification tasks.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Brain tumors are masses or abnormal growths of cells within the brain or the
central spinal canal with symptoms such as headaches, seizures, weakness or
numbness in the arms or legs, changes in personality or behaviour, nausea,
vomiting, vision or hearing problems and dizziness. Conventional diagnosis of
brain tumour involves some tests and procedure which may include the
consideration of medical history, physical examination, imaging tests (such as
CT or MRI scans), and biopsy (removal and examination of a small piece of the
tumor tissue). These procedures, while effective, are mentally strenuous and
time demanding due to the manual examination of the brain scans and the
thorough evaluation of test results. It has been established in lots of medical
research that brain tumours diagnosed and treated early generally tends to have
a better prognosis. Deep learning techniques have evolved over the years and
have demonstrated impressive and faster outcomes in the classification of brain
tumours in medical imaging, with very little to no human interference. This
study proposes a model for the early detection of brain tumours using a
combination of convolutional neural networks (CNNs) and extreme gradient
boosting (XGBoost). The proposed model, named C-XGBoost has a lower model
complexity compared to purely CNNs, making it easier to train and less prone to
overfitting. It is also better able to handle imbalanced and unstructured data,
which are common issues in real-world medical image classification tasks. To
evaluate the effectiveness of the proposed model, we employed a dataset of
brain MRI images with and without tumours.
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