Developing Predictive Models for Early Detection of Brain Tumors Using Medical Imaging Data

EasyChair Preprint 13588

Abstract

Early detection of brain tumors is crucial in improving patient outcomes and survival rates. Medical imaging, such as MRI and CT scans, provides valuable insights into the presence and characteristics of brain tumors. However, interpreting these images can be time-consuming and subjective, leading to potential diagnostic errors. This abstract highlights the significance of developing predictive models for the early detection of brain tumors using medical imaging data.

The process begins with data collection and preprocessing, ensuring the quality and consistency of the imaging data while protecting patient privacy. Feature selection and extraction techniques are then applied to identify relevant features from the medical images, leveraging image processing algorithms and incorporating clinical data. Machine learning algorithms are employed to develop and train predictive models using preprocessed data, optimizing model performance through hyperparameter tuning and cross-validation.

Model evaluation and validation are essential to assess the accuracy and reliability of the predictive models. Performance metrics such as accuracy, sensitivity, specificity, and area under the curve are used to compare different models and select the most effective one. Validation on independent datasets enhances the generalizability of the models and helps identify potential overfitting or bias.

Keyphrases: Brain tumors, Ethical Considerations, early detection, imaging data, medical, predictive models

@booklet{EasyChair:13588,
  author    = {Elizabeth Henry and Harold Jonathan},
  title     = {Developing Predictive Models for Early Detection of Brain Tumors Using Medical Imaging Data},
  howpublished = {EasyChair Preprint 13588},
  year      = {EasyChair, 2024}}