| Autor: |
Dua, Saurabh, Chakravarthy, V. Deeban, Sharma, Ishita |
| Předmět: |
|
| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3075 Issue 1, p1-9, 9p |
| Abstrakt: |
Brain tumors and other cancers of the nervous system are among the primary causes of life's loss in today's world. The growth of abnormal cells in the brain can result in brain tumors, which are classified as either benign or malignant. While detecting brain tumors at an early stage is crucial for improving patient outcomes, it can be challenging due to the lack of early symptoms and the complexity of the brain's structure. Machine learning has emerged as a promising tool for detecting brain tumors accurately. In recent years, researchers have developed various machine learning models that utilize image analysis techniques to identify brain tumors based on CT and MRI scans. In this research paper, we compare the performance of several ML algorithms on a Brain Tumor Dataset to evaluate and analyze appropriate algorithm for correctly identifying the presence of a tumor in the brain. We analyzed the efficiency of the algorithms based on metrics such as sensitivity, specificity, accuracy, and false positive rate. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
