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<p>As a major global health challenge, the progressive cognitive decline and memory loss associated with Alzheimer's disease (AD) significantly impact individuals' quality of life. Due to the lack of a definitive cure, early and precise diagnosis remains crucial for implementing effective intervention and management strategies. In this study, we introduce a pioneering approach utilizing Transformer based ResLadderNet for AD classification, achieving an exceptional accuracy of 98%. The classification of AD is crucial for several reasons. Firstly, accurate classification aids in early detection, allowing healthcare professionals to implement interventions aimed at mitigating disease progression and enhancing patient outcomes. Secondly, it allows for stratification of patients based on disease severity and subtype, which is essential for personalized treatment planning and clinical trials. Moreover, effective classification supports research efforts aimed at understanding disease mechanisms and identifying biomarkers that could further enhance diagnostic accuracy and therapeutic development. The ResLadderNet architecture leverages the strengths of residual networks and ladder networks, optimizing feature extraction and hierarchical representation learning with transformer to feature propagation. This approach not only enhances the interpretability of the model's decisions but also improves its robustness against variations in imaging data quality and disease progression stages. Our methodology employs a meticulously curated dataset of 10,240 samples across four distinct classes, sourced from Harvard, to train and validate the model. This model achieves outstanding performance with an accuracy of 98.93%. Notably, this study demonstrates remarkable performance, achieving precision at 0.98, recall at 0.97, and an F1 score of 0.98, underscoring its efficacy in detecting early indicators of Alzheimer's disease. This breakthrough has significant implications for early intervention and treatment, potentially improving the lives of millions worldwide.</p>

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Enhancing Alzheimer's Disease Classification using Transformer based Neuroimaging Technique

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月10日

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  • Kumar Janardan Patra,
  • Jibitesh Mishra,
  • Sanjit Kumar Dash,
  • Sudhir Kumar Mohapatra

摘要

As a major global health challenge, the progressive cognitive decline and memory loss associated with Alzheimer's disease (AD) significantly impact individuals' quality of life. Due to the lack of a definitive cure, early and precise diagnosis remains crucial for implementing effective intervention and management strategies. In this study, we introduce a pioneering approach utilizing Transformer based ResLadderNet for AD classification, achieving an exceptional accuracy of 98%. The classification of AD is crucial for several reasons. Firstly, accurate classification aids in early detection, allowing healthcare professionals to implement interventions aimed at mitigating disease progression and enhancing patient outcomes. Secondly, it allows for stratification of patients based on disease severity and subtype, which is essential for personalized treatment planning and clinical trials. Moreover, effective classification supports research efforts aimed at understanding disease mechanisms and identifying biomarkers that could further enhance diagnostic accuracy and therapeutic development. The ResLadderNet architecture leverages the strengths of residual networks and ladder networks, optimizing feature extraction and hierarchical representation learning with transformer to feature propagation. This approach not only enhances the interpretability of the model's decisions but also improves its robustness against variations in imaging data quality and disease progression stages. Our methodology employs a meticulously curated dataset of 10,240 samples across four distinct classes, sourced from Harvard, to train and validate the model. This model achieves outstanding performance with an accuracy of 98.93%. Notably, this study demonstrates remarkable performance, achieving precision at 0.98, recall at 0.97, and an F1 score of 0.98, underscoring its efficacy in detecting early indicators of Alzheimer's disease. This breakthrough has significant implications for early intervention and treatment, potentially improving the lives of millions worldwide.