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Relative likelihood based aggregated dual deep neural network for skin lesion recognition in dermoscopy images

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Abstract

This paper presents a novel approach classifying benign and malignant skin lesions based on dermoscopy images using a deep learning (DL) framework. We present a dual deep neural network (DDNN) to solve the problem of classification accuracy (ACC) that varies from class to class, which happens when different feature sets work better for different classes. This DDNN comprises two deep neural networks (DNN) designed to extract and characterize lesions, utilizing two distinct handcrafted feature sets: phase congruency features and (PC) Gabor features (GA). PC is known for its resilience to illumination and contrast variations, and GA captures high-frequency directional information in localized regions surrounding the lesion. The DDNN outputs of these two-trained networks are aggregated, employing a novel likelihood-based add-and-compare decision (LACD) in the output layer. With the utilization of training data, this proposed method generates additional discriminative features that aid in the classification of malignant and benign. To evaluate the effectiveness of our approach, ablation studies and experiments were conducted using skin cancer images from the ISIC archive, achieving a normalised ACC of 87.76% for ISIC2016 and 84.25% for ISIC2017. The performance of our method is also compared with existing skin lesion classification methods.

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Authors and Affiliations

  1. Mepco Schlenk Engineering College, Sivakasi, India

    S. Anand, A. Sheeba & M. K. Maha Tharshini

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Anand, S., Sheeba, A. & Maha Tharshini, M.K. Relative likelihood based aggregated dual deep neural network for skin lesion recognition in dermoscopy images. Multimed Tools Appl 83, 60603–60626 (2024). https://doi.org/10.1007/s11042-023-17908-z

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