Cheryl Angelica*
| Derwin Suhartono |
© 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
This research introduces a novel approach to Alzheimer's disease detection by combining Xception's efficiency with machine learning classifiers, notably XGBoost. The hybrid model strategically uses Xception for feature extraction and integrates machine learning algorithms to enhance early detection accuracy, leveraging depthwise separable convolution for reduced computational complexity. Addressing imbalanced data, the study incorporates SMOTE, showcasing the hybrid model's effectiveness. Before SMOTE, the model achieved 72.89% accuracy and a 74.35% F1 score, outperforming the non-hybrid Xception model. Post-SMOTE, accuracy increases to 86.75%, and the F1 score to 86.84%, demonstrating substantial improvement without excessive computational demands. In comparison, the non-hybrid Xception model exhibits 78.71% accuracy and a 78.27% F1 score after SMOTE, emphasizing the pronounced enhancement achieved by the hybrid model. The Kaggle-derived dataset, totaling 6400 images, undergoes meticulous preprocessing, acknowledging dataset-specific constraints on generalizability. Emphasizing the importance of addressing data imbalance for robust classification, the hybrid model offers a promising solution for accurate and efficient Alzheimer's disease detection. This study contributes valuable insights to the field, showcasing the potential of innovative hybrid models to address complex healthcare challenges while balancing accuracy and computational efficiency.
Alzheimer’s disease, hybrid model, depthwise separable convolution, extreme gradient boost, support vector machine, random forest, xception, health care
In the realm of human memory, three fundamental components play pivotal roles: working memory, short-term memory, and long-term memory, each serving distinct functions in the cognitive processes. Working memory facilitates attention and concentration during the intake of data and information, while short-term memory temporarily stores information for immediate use. In contrast, long-term memory serves as the repository for a lifetime of experiences [1]. However, this intricate system of memory is susceptible to various maladies, with Alzheimer's disease representing a significant and devastating affliction. As a progressive neurodegenerative disorder, Alzheimer's erodes memory, cognitive abilities, and even basic daily functioning. It constitutes the predominant form of dementia, contributing to a substantial percentage of dementia cases globally, and its prevalence is poised to rise exponentially. The World Alzheimer's Report of 2015 underscores a grave global concern, with over 50 million individuals grappling with dementia across the world, a number that is poised to double every two decades, as visually represented in Figure 1. This alarming trend is not isolated to the global stage; Indonesia, for instance, presented recent statistics in 2022 revealing a staggering 1.2 million of its citizens contending with the challenges of Alzheimer's disease [2]. Regrettably, a definitive cure remains elusive, as the disease continues to ravage brain cells [3]. Nonetheless, early detection holds promise in enabling medical professionals, particularly doctors, to explore interventions that may temporarily ameliorate symptoms, slow disease progression, and mitigate neural damage.
Figure 1. Statistics of dementia over the world
Deep learning (DL) holds the transformative potential to revolutionize medical diagnostics, and within this domain, convolutional neural networks (CNNs), a subset of deep learning algorithms, have exhibited promise in the direct diagnosis of Alzheimer's disease using medical imaging data [4]. The LeNet-5 design was used in an earlier study by Sarraf and Tofighi [5], which also highlighted the necessity for more convolutional neural layers to enhance the accuracy of Alzheimer's disease diagnosis using MRI scans. However, traditional CNN models face limitations, including significant computational demands and a high number of training parameters, necessitating expensive computing resources.
To address these challenges, this work proposes a hybrid model that combines Xception, a cutting-edge deep learning architecture introduced by Chollet in 2017, with machine learning strategies for Alzheimer's disease classification [6]. Notably, prior studies have shown the efficacy of various models but often encounter difficulties related to imbalanced data and expensive computing resources. The suggested hybrid approach aims to leverage the strengths of both Xception and machine learning, providing a more robust and efficient solution for Alzheimer's classification. This research significantly contributes by explicitly addressing the drawbacks of previous methodologies, potentially advancing Alzheimer's disease detection and improving patient care.
Chollet's introduction of Xception in 2017 is noteworthy, combining residual connections and Depthwise Separable Convolution (DSC) for increased accuracy and reduced computing complexity [6]. Compared to conventional convolutional layers, DSC employs fewer parameters and computational calculations while maintaining an equivalent level of performance [7]. The central research question guiding this study is: How can a hybrid model, combining Xception and machine learning techniques, improve the efficiency of Alzheimer's disease detection compared to traditional CNN models?
To address this question, the research objectives include evaluating the effectiveness of the proposed hybrid models compared to non-hybrid models, specifically Xception alone. The hybrid models, incorporating Xception and machine learning techniques, will be systematically compared with the non-hybrid model to comprehensively evaluate their effectiveness. By combining the advantages of both methods, the proposed model aims to overcome the limitations of traditional CNN models, offering improved F1-Scores and reduced computing complexity.
Additionally, the challenge of imbalanced data is addressed through the application of the Synthetic Minority Over-sampling Technique (SMOTE), notably enhancing the performance of the selected models. This comprehensive approach ensures valuable insights into the most suitable method for Alzheimer's detection, furthering the understanding and advancement of diagnostic methodologies in the field.
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