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<p>Early diagnosis is a crucial aspect in the current healthcare landscape, enabling timely intervention of diseases through medical image-based detection and classification. However, these imaging modalities' intrinsic variability and complexity pose significant challenges to existing deep learning methods. A single model, which must handle such diversity in image types, including differences in resolution, contrast, and anatomical structures, must deal with inconsistencies in detection performance and classification accuracy. This paper presents a new approach to these challenges by developing a unified deep learning framework that integrates the CoCapsule Vision Transformer Network (CoCaps-VisNet) for disease detection and classification with the Flamingo Multitracker (FlaMT) for gradient estimation. CoCaps-VisNet simultaneously leverages the representation learning capacity of Capsule Networks and the attention mechanism in Vision Transformers to fructify complex spatial relations and hierarchies from medical images. This hybrid architecture will yield robust results for a wide range of imaging modalities, complementing some of the current deep learning approaches that often fail to generalize beyond a specific modality of medical images. This is a novel attempt, as far as the researcher is concerned, where efforts have been made to integrate FlaMT with CoCaps-VisNet and create a framework for their integration to handle the diversified characteristics of medical imaging data effectively. Compared to existing methods focusing on a single imaging modality, the proposed method is more generalized and adaptable, yielding consistent results across X-ray, CT, and MRI datasets. This effort will bring transformative medical image analysis through a unified, highly flexible framework for enhancing disease detection and classification across multiple imaging modalities.</p>

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An integrative Flamingo multitracker model capsule Vision Transformer for medical image-based disease diagnosis

  • 期刊论文

  • 发表日期：

    2025年7月14日

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  • Maheswari Gunasekaran,
  • Gopalakrishnan Subburayalu

摘要

Early diagnosis is a crucial aspect in the current healthcare landscape, enabling timely intervention of diseases through medical image-based detection and classification. However, these imaging modalities' intrinsic variability and complexity pose significant challenges to existing deep learning methods. A single model, which must handle such diversity in image types, including differences in resolution, contrast, and anatomical structures, must deal with inconsistencies in detection performance and classification accuracy. This paper presents a new approach to these challenges by developing a unified deep learning framework that integrates the CoCapsule Vision Transformer Network (CoCaps-VisNet) for disease detection and classification with the Flamingo Multitracker (FlaMT) for gradient estimation. CoCaps-VisNet simultaneously leverages the representation learning capacity of Capsule Networks and the attention mechanism in Vision Transformers to fructify complex spatial relations and hierarchies from medical images. This hybrid architecture will yield robust results for a wide range of imaging modalities, complementing some of the current deep learning approaches that often fail to generalize beyond a specific modality of medical images. This is a novel attempt, as far as the researcher is concerned, where efforts have been made to integrate FlaMT with CoCaps-VisNet and create a framework for their integration to handle the diversified characteristics of medical imaging data effectively. Compared to existing methods focusing on a single imaging modality, the proposed method is more generalized and adaptable, yielding consistent results across X-ray, CT, and MRI datasets. This effort will bring transformative medical image analysis through a unified, highly flexible framework for enhancing disease detection and classification across multiple imaging modalities.