Automated Diagnosis of Multiple Sclerosis Using Transfer Learning and LightGBM on FLAIR MRI Data
Bibliographic record
Abstract
This study explores the application of transfer learning (TL) combined with a Light Gradient Boosting Machine (LightGBM) for classifying and detecting multiple sclerosis (MS) lesions in FLAIR Magnetic Resonance Imaging (MRI) images.Utilizing a dataset of 3,427 MRI images categorized into four distinct classes-Control Axial, Control Sagittal, MS-Axial, and MS-Sagittal-preprocessing included image resizing, normalization, and conversion to ensure consistency and compatibility for training.TL architectures including DenseNet169, VGG16, ResNet50, InceptionV3, and MobileNet were fine-tuned to extract meaningful image features.LightGBM was subsequently employed to classify these features with high efficiency.Among the evaluated model combinations, DenseNet169 paired with LightGBM achieved the best performance, with an accuracy of 98.4%, precision of 0.98, recall of 0.98, and an F1 score of 0.98.VGG16 and ResNet50 also demonstrated robust classification capabilities with accuracies of 97.7% and 95.3%, respectively.In contrast, MobileNet+LightGBM (accuracy: 94.0%, F1-score: 0.94) and InceptionV3+LightGBM (accuracy: 90.8%, F1-score: 0.91) exhibited lower performance, reflecting limited effectiveness in capturing intricate MRI patterns.Receiver Operating Characteristic (ROC) curves validated the superior discriminatory power of DenseNet169+LightGBM.These results highlight the potential of combining transfer learning (TL) and machine learning (ML) for accurate MS lesion identification and early diagnosis, supporting improved clinical tools.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".