# Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection

**Authors:** Muyiwa Babayomi, Oluwatosin Atinuke Olagbaju, Abdulrasheed Adedolapo Kadiri  
**arXiv:** [2301.02317](https://arxiv.org/abs/2301.02317)  
**Date:** 2023-01-05  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2301.02317v1/

## Key points

- Brain tumors are abnormal growths of cells within the brain or central spinal canal
- Symptoms of brain tumors include headaches, seizures, weakness or numbness in the arms or legs, changes in personality or behavior, nausea, vomiting, vision or hearing problems, and dizziness
- Conventional diagnosis of brain tumors involves medical history consideration, physical examination, imaging tests (CT or MRI scans), and biopsy
- Machine learning techniques have shown promise in classifying brain tumors using medical imaging data
- The C-XGBoost model combines convolutional neural networks (CNNs) with extreme gradient boosting (XGBoost) for early detection of brain tumors
- C-XGBoost has lower complexity compared to purely CNNs and handles imbalanced and unstructured data effectively
- Machine learning is increasingly important in biomedical sciences for analyzing high-dimensional and multimodal data
- Current approaches face challenges in incorporating structural and functional imagery as well as genomic, proteomic, and ancillary data
- Machine learning-aided diagnosis aims to assist doctors in disease diagnosis using artificial intelligence (AI) and deep learning techniques
- Neural networks excel at prediction problems involving unstructured data like images but face barriers in segmentation, radiomics analysis, and various detection methods relevant for early treatment and prognosis.

## Summary

Brain tumors are abnormal growths of cells within the brain or central spinal canal. They can cause a variety of symptoms such as headaches, seizures, weakness or numbness in the arms or legs, changes in personality or behavior, nausea, vomiting, vision or hearing problems, and dizziness. The conventional diagnosis of brain tumors involves various tests and procedures including medical history consideration, physical examination, imaging tests (such as CT or MRI scans), and biopsy. However, these procedures can be mentally strenuous and time-consuming due to the manual examination of brain scans and the thorough evaluation of test results. Early detection of brain tumors is crucial for better prognosis and treatment outcomes. Machine learning techniques have shown promising results in the classification of brain tumors using medical imaging data. In particular, convolutional neural networks (CNNs) combined with extreme gradient boosting (XGBoost) have been proposed as a model for early detection. This model, called C-XGBoost, has lower complexity compared to purely CNNs which makes it easier to train and less prone to overfitting. It also handles imbalanced and unstructured data more effectively. The literature highlights that machine learning has become increasingly important in biomedical sciences for analyzing high-dimensional and multimodal data. Current approaches face challenges in incorporating structural and functional imagery as well as genomic, proteomic,and ancillary data. Researchers are interested in understanding brain-based learning systems by studying the human brain's flexibility and power. Machine learning-aided diagnosis aims to assist doctors in disease diagnosis using artificial intelligence (AI) and deep learning techniques. For example,a study examined the use of CT image characteristics along with blood tumor markersand a CNN algorithm to diagnose pancreatic cancer.Neural networks excel at prediction problems involving unstructured data like images; however they face barriers in segmentation,radiomics analysis,and various detection methods relevant for early treatment and prognosis. In summary, this study proposes a refined model called C-XGBoost for the early detection of brain tumors using a combination of CNNs and XGBoost. The model addresses the challenges faced by current approaches and demonstrates effectiveness in handling imbalanced and unstructured medical image data. Early detection of brain tumors is crucial for better treatment outcomes, and machine learning techniques offer promising solutions in this field.