An Ensemble Deep Learning Approach for Enhanced Classification of Pituitary Tumors
Bibliographic record
Abstract
Tumor detection has emerged as a significant aspect of neuro-oncology and neuroradiology, with critical importance in improving patient survival rates. Tumors, whether benign (non-cancerous) or malignant (cancerous), can result in severe morbidity, and their accurate detection is very important for treatment. In recent years, medical imaging modalities such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have been extensively utilized for non-invasive tumor detection. These imaging techniques provide in-depth information about the tumor's location, size, and morphology, which is pivotal for diagnosing and planning therapeutic interventions. However, the manual interpretation of these imaging modalities is time-intensive and susceptible to human inaccuracies. Moreover, the subtle features of tumors can be easily missed in the manual assessment. Hereby, we propose an ensemble deep learning approach to classify pituitary tumors, based on the weighted average technique that incorporates three base deep learning models: ResNet 152, DenseNet 201, and VGG 16. Moreover, we implement the Segment Anything Model (SAM) to perform segmentation to our dataset and then execute the ensemble model to classify pituitary tumors from normal/healthy brain images. We compare our proposed approach using segmented data and non-segmented data, finding that the segmented data outperforms the non-segmented data by a margin of 1.77%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".