MemAF-KTCBM: a Memory-Aware Fractional optimisation-enabled deep learning model for atherosclerosis classification.

Atherosclerosis, a chronic vascular disorder caused by the abnormal functioning of blood vessels, remains a major contributor to cardiovascular diseases. Although recent advances in machine learning and deep learning have improved atherosclerosis classification, several critical challenges persist, particularly class imbalance, high computational complexity, limited interpretability, and poor generalisation, which constrain their clinical utility. To address these limitations, this paper proposes a novel Memory-Aware Fractional optimisation-assisted Knowledge-distilled mutual-conditioned dynamic Transformer-enabled Convolutional neural network-bidirectional long short-term Memory (MemAF-KTCBM) framework that employs knowledge distillation and mutual information fusion to reduce computational complexity while preserving classification performance. Initially, coronary artery structures are segmented using a memory-aware fractional optimisation-enabled Modified Pyramid Scene Parsing Network to improve classification reliability. Furthermore, a Memory-Aware Fractional optimisation-enabled Style Generative Adversarial Network is employed to generate high-quality synthetic angiographic images, thereby mitigating class imbalance and reducing prediction bias. In addition, pretrained local descriptors are incorporated to enhance feature extraction, interpretability, and classification reliability. The proposed framework is evaluated using the ARCADE and CADICA datasets using multiple training percentages and 10-fold cross-validation. On the ARCADE dataset, the MemAF-KTCBM model achieved an accuracy of 97.53%, a precision of 97.82%, a recall of 96.94%, an NPV of 0.97, a PPV of 0.98, and an MCC of 0.95. Similarly, on the CADICA dataset, it attained an accuracy of 96.65%, a precision of 96.94%, a recall of 96.07%, an NPV of 0.97, a PPV of 0.97, and an MCC of 0.94. These findings confirm that the proposed framework acts as a robust and effective solution for atherosclerosis classification across heterogeneous atherosclerosis datasets.