Enhancing alzheimer’s diagnosis through optimized brain lesion classification in MRI with attention-driven grid feature fusion

Article type: Research Article

Authors: Mohanty, Manas Ranjan^a | Mallick, Pradeep Kumar^{a; *} | Navandar, Rajesh Kedarnath^b | Chae, Gyoo-Soo^c | Jagadev, Alok Kumar^a

Affiliations: [a] School of Computer Engineering, KIIT Deemed to be University, Odisha, India | [b] Department of Electronic and Telecommunication Engineering, JSPM Jayawantrao Sawant College of Engineering Hadaspar, Pune, India | [c] Division of Advanced IT, Baekseok University, Korea

Correspondence: [*] Corresponding author: Pradeep Kumar Mallick, School of Computer Engineering, KIIT Deemed to be University, Odisha. E-mail: pradeep.mallickfcs@kiit.ac.in.

Abstract: This paper explores cognitive interface technology, aiming to tackle current challenges and shed light on the prospects of brain-computer interfaces (BCIs). It provides a comprehensive examination of their transformative impact on medical technology and patient well-being. Specifically, this study contributes to addressing challenges in classifying brain lesion images arising from the complex nature of lesions and limitations of traditional deep learning approaches. It introduces advanced feature fusion models that leverage deep learning algorithms, including the African vulture optimization (AVO) algorithm. These models integrate informative features from multiple pre-trained networks and employ innovative fusion techniques, including the attention-driven grid feature fusion (ADGFF) model. The ADGFF model incorporates an attention mechanism based on the optimized weights obtained using AVO. The objective is to improve the overall accuracy by providing fine-grained control over different regions of interest in the input image through a grid-based technique. This grid-based technique divides the image into vertical and horizontal grids, simplifying the exemplar feature generation process without compromising performance. Experimental results demonstrate that the proposed feature fusion strategies consistently outperform individual pre-trained models in terms of accuracy, sensitivity, specificity, and F1-score. The optimized feature fusion strategies, particularly the GRU-ADGFF model, further enhance classification performance, outperforming CNN and RNN classifiers. The learning progress analysis shows convergence, indicating the effectiveness of the feature fusion strategies in capturing lesion patterns. AUC-ROC curves highlight the superior discriminatory capabilities of the ADGFF-AVO strategy. Five-fold cross-validation is employed to assess the performance of the proposed models, demonstrating their accuracy, and few other accuracy-based measures. The GRU-ADGFF model optimized with AVO consistently achieves high accuracy, sensitivity, and AUC values, demonstrating its effectiveness and generalization capability. The GRU-ADGFF model also outperforms the majority voting ensemble technique in terms of accuracy and discriminative ability. Additionally, execution time analysis reveals good scalability and resource utilization of the proposed models. The Friedman rank test confirms significant differences in classifier performance, with the GRU-ADGFF model emerging as the top-performing method across different feature fusion strategies and optimization algorithms.

Keywords: Cognitive-interface, brain-computer interfaces (BCIs), brain lesion classification, CNN’s pre-trained networks, attention-driven feature fusion, attention-driven grid-based feature fusion, African vulture optimization (AVO), recurrent neural network (RNN), gated recurrent unit (GRU)

DOI: 10.3233/IDT-240652

Journal: Intelligent Decision Technologies, vol. 18, no. 3, pp. 1993-2018, 2024