Brain tumour MRI detection and classification based on the convolutional neural network
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) has emerged as a widely used diagnostic technique for brain tumour detection. However, the diagnosis of brain tumours poses significant challenges due to their occurrence in diverse locations and various types. Furthermore, MRI generates images that require manual analysis by physicians, which can be laborious and prone to errors. To enhance the efficacy and accuracy of brain tumour detection, recent advances in artificial intelligence have led to the development of machine learning algorithms. In this study, a convolutional neural network (CNN) based method was proposed for brain tumour detection and classification through the preprocessing of raw MRI images. The customized CNN model achieves an accuracy of 98% on a dataset consisting of four types of MRI images, including three types of brain tumours and healthy brain images, with preprocessing applied to all images. The CNN model demonstrates an accuracy of 95% in classifying raw MRI images from the dataset. The CNN model's performance is further improved by training the model with preprocessed images that have been transformed into the same colour space and object area zoomed in. These findings provide a promising avenue for the development of automated and efficient brain tumour detection systems using CNN and MRI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".