Abstract
This study investigates the advantages of an algorithm for detecting brain tumors using magnetic resonance imaging. The thematic analysis demonstrates how the algorithm can be understood and changed through narrative descriptions. The findings highlight areas for improvement, which aids in the direction of future research. Based on unexpected results, the algorithm was improved over time. Even though the study had some restrictions and limitations, this makes the algorithm a versatile tool for detecting brain tumors. This study is an important step toward better understanding algorithmic applications and demonstrates the significance of qualitative insights in shaping the future of brain tumor detection methods.
Reference32 articles.
1. "Brain cancer & brain tumor: Symptoms, causes & treatments," 2022. Accessed: 16 Dec. 2023.
2. "Brain tumors - classifications, symptoms, diagnosis and treatments," 2019. Accessed: 16 Dec. 2023.
3. A. C. Society, "Mri for cancer," n.d. Accessed: February 2, 2024.
4. R. Kaifi, "A review of recent advances in brain tumor diagnosis based on ai-based classification," Diagnostics, vol. 13, no. 18, p. 3007, 2023.
5. S. Saeedi, S. Rezayi, H. Keshavarz, and S. R. Niakan Kalhori, "Mri-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques," BMC Medical Informatics and Decision Making, vol. 23, no. 1, p. 16, 2023.