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    This study enhances computer-aided diagnosis (CAD) for skin cancer by improving multimodal models to handle missing patient metadata. The new MetaBlock extension boosts diagnostic accuracy for skin lesion classification.

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    Area of Science:

    • Dermatology
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Skin cancer is a prevalent global health concern, posing challenges for healthcare systems.
    • Computer-aided diagnosis (CAD) systems show promise for skin cancer detection, especially in underserved areas.
    • Multimodal deep learning models integrating imaging and patient metadata improve diagnostic accuracy but struggle with incomplete data.

    Purpose of the Study:

    • To propose an enhanced Metadata Processing Block (MetaBlock) architecture for multimodal skin cancer classification.
    • To address the challenge of missing or incomplete metadata in deep learning models.
    • To improve the robustness and accuracy of CAD systems for skin lesion detection.

    Main Methods:

    • An extension of the MetaBlock architecture was developed, incorporating a sentence embedding algorithm.
    • The sentence embedding algorithm generates dense vectors to capture semantic relationships within metadata features.
    • The proposed method was evaluated on the PAD-UFES-20 dataset and an extended version using ResNet-50 backbone.

    Main Results:

    • The enhanced MetaBlock method outperformed the original architecture across all experimental scenarios.
    • Balanced accuracy reached up to 70.2 ± 2.8% on PAD-UFES-20 and 68.2 ± 1.0% on the extended dataset.
    • The new approach demonstrates improved performance in handling missing metadata for skin cancer classification.

    Conclusions:

    • The proposed MetaBlock extension offers a more robust solution for multimodal skin cancer classification, particularly with incomplete metadata.
    • The method is computationally efficient and simple to implement, making it suitable for real-world CAD systems.
    • This work advances the development of reliable AI tools for dermatological diagnosis in clinical settings.