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February 8, 2026PeerJ Computer Science1 citationsOpen Access

Moth-flame optimized UNet++ with self-attention for early-stage Alzheimer’s disease prediction using multimodal input

KVKrishna Kumar V GMGeetha Devasena MS SBSaravana Balaji BJain University

Key Points

The aim is to develop a framework for early-stage Alzheimer’s Disease prediction using multimodal inputs and advanced optimization techniques.
Developed a Moth Flame Optimized UNet++ framework with self-attention for analyzing multi-modal neuroimaging data.
Pre-processed sMRI and PET images for denoising and texture pattern extraction.
Implemented support vector machine, k-nearest neighbors, and Random Forest for classification.
Used weighted stacking ensemble methods to improve predictive performance.
Evaluated using metrics such as precision, recall, F1-score, Accuracy, and AUC-ROC.
Achieved precision of 90.5%, recall of 89.9%, F1-score of 90.2%, and Accuracy of 91.8%.
Attained an AUC-ROC value of 94.1%, indicating strong diagnostic performance.
Outperformed existing multi-modal ensemble frameworks in multiple performance metrics.

Abstract

Early-stage identification of Alzheimer’s Disease (AD) is a sizeable challenge to health care globally due to its progressive nature and the fact that there is no available effective treatment. It becomes strategic for practices using interventions meant to halt or reverse cognitive decline if diagnosed early. Recent medical imaging advancements, mainly Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), have greatly unveiled subtle pathological changes associated with this disease. Studies have shown that multimodal neuroimaging can provide crucial information regarding the structural and functional changes in the brain that are associated with AD. However, more research is required to establish sustainable techniques for the detection of AD at all its stages. In this research, a framework of Moth Flame Optimized UNet++ with self-attention is proposed to analyze multi-modal inputs of Structural Magnetic Resonance Imaging (sMRI), PET, and neuropsychological test data for the classification of Alzheimer’s disease ( UNet + + SA –MFO). The framework involves pre-processing the sMRI and PET images to denoise, skull strip, denormalization, next captures complex texture patterns and spatial relationships from both images by placing attention gates at skip connections to ensure reduction of irrelevant features and enhanced localization of significant features. Neuropsychological assessments are passed through fully connected layers of UNet++. Moth Flame Optimization optimizes hyperparameters. Then, a fused feature set is created as a concatenation of all features from multi-modal inputs. Support vector machine (SVM), k-nearest neighbors (k-NN), and Random Forest are used to model, and later weighted stacking ensemble is used to predict the output. The framework is implemented in Python, and evaluation metrics like precision, recall, F1-score, Accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC) are analyzed. UNet++ SA –MFO attains effective 90.5%, 89.9%, 90.2%, 91.8% and 94.1% precision, recall, F1-score, Accuracy and AUC-ROC against existing multi-modal ensemble frameworks. The above findings highlight the potential of enhanced UNet++ augmented with self-attention-based feature extraction and the benefit of integrating innovative optimization for more precise Alzheimer’s Disease diagnosis and classification at the early stage. This proposed framework provides valuable contributions to Alzheimer’s Disease pathology insight, improved diagnostic sensitivity, and ultimately, improved management of this neurological disease.

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