Recent Trends on Brain Tumor Detection Using Hybrid Deep Learning Methods
Bibliographic record
Abstract
The term "brain tumor" describes the unregulated increase in brain cells, which can have various adverse consequences. In the field of medical research, a variety of methods are employed to find brain tumor and the most reliable method still utilized by specialists is Magnetic Resonance Imaging (MRI). The noninvasive MRI method has developed into a primary emission brain tumor investigative tool. In order to accurately identify the extent of tumor, reliable, entirely an automatic segmentation method for the brain tumor and this is still being investigated. There is a higher possibility of success for the treatment when tumors are found early. Detecting brain tumor affected cells is tedious and time-consuming process. Identification and classification of brain tumors at the earliest is very essential for effective treatment. This article conducted an analysis of existing methodologies to apply various forms of deep learning techniques to MRI data. This review provides hybrid deep learning based brain tumor diagnosis approach which combines different deep learning methods like Convolutional Neural Networks (CNN), UNET Architecture, GoogLeNet and Gabor Filter for feature extraction. From extensive survey, this review concludes that deep learning approaches provide more accurate and efficient results than traditional machine learning algorithms. This survey highlights the current clinical challenges, potential future solutions and opens up the researcher's challenges to evolve systematic brain tumor detection system demonstrating clinically acceptable better accuracy which will assist the radiologists in diagnosis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".