The Role of Machine Learning in the Detection and Classification of Brain Tumors: A Literature Review of the Past Two Years
Bibliographic record
Abstract
A brain tumor is an abnormal growth of cells in the brain. There are four common types of brain tumors. Doctors can segment and identify the tumors manually, but it is very time-consuming. There exist automatic segmentation algorithms that can facilitate the process. Deep learning is a new method of creating powerful AI models. As a result, there is a need for automatic segmentation algorithms that can facilitate the process and improve the accuracy of brain tumor detection. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for developing such algorithms. In particular, deep learning (DL) methods, such as convolutional neural networks (CNNs), have shown great potential for accurately identifying brain tumors in medical images. This paper presents a literature review of recently published papers (2020-2022) on brain tumor classification and detection using artificial intelligence. The review covers various AI and DL methods, including supervised learning, reinforcement learning, and unsupervised learning. It evaluates their effectiveness in detecting and classifying brain tumors in medical images. The review also discusses the challenges and limitations of these methods, as well as future directions for research in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".