MRI‐Based Heart Disease Diagnosis: An Automated IBNet9X‐DenseNet8X Framework for Optimal Feature Fusion and Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Cardiovascular diseases (CVDs) remain a leading global health challenge, emphasising the need for advanced diagnostic systems that enable accurate detection and classification. This study presents a novel deep learning framework based on a dense and inverted bottleneck residual mechanism, integrating two custom convolutional neural network architectures: an 8‐block Dense model and a 9‐block Inverted Bottleneck Layered model. These models are designed to extract multiscale features from cardiac MRI scans for precise classification across five CVD categories. We enhance image quality using Contrast‐Limited Adaptive Histogram Equalisation (CLAHE) in the preprocessing pipeline and apply data augmentation to address class imbalance and promote model generalisation. Atom Search optimisation (ASO) is employed to reduce feature dimensionality while retaining critical information, and a serial feature fusion strategy is used to integrate the optimised feature vectors. Classification is performed using several neural network variants (Narrow NN, Medium NN, Wide NN, and Bi‐Layered NN). The proposed method is evaluated on both the Automated Cardiac Diagnosis Challenge (ACDC) and Sunnybrook Cardiac Data (SCD) datasets, achieving superior accuracies of 97.3% and 96.8%, respectively, compared to state‐of‐the‐art techniques, demonstrating its effectiveness for cardiovascular disease diagnosis.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it