A Deep Learning Framework Based on DenseNet Architecture for Accurate Diagnosis of Neurodegenerative Diseases Using Multimodal Medical Imaging Data
Bibliographic record
Abstract
This paper presents a deep learning framework, which utilizes DenseNet structure, to diagnose neurodegenerative diseases using multimodal medical images accurately. The new design takes advantage of the efficient feature interdependency and gradients of DenseNet and uses them to enhance normal to abnormal brain status classification. The performance was analyzed at length with various indices. The model had an Area Under the Curve (AUC) of 0.88 which implies high discriminatory power. The confusion matrix lifted a high level of true positive rate with minimum false negative rate especially in abnormal cases. To display the plot of the separability of classes in the feature space, the dimensionality reduction (PCA and t-SNE) were applied. Moreover, precision-recall analysis and training-validation curves proved the strength and generalizing of the model. The outcomes confirm the utility of DenseNet-related designs in healthcare diagnostics and point to the promise of the incorporation of deep learning in clinical practice in the early-stage diagnosis of neurodegenerative disorders.
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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".