An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification

Desale, R. P.; Patil, Pravin

doi:10.1080/13682199.2023.2229018

Cited by 4 publications

(1 citation statement)

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“…Numerous studies have utilized attention mechanisms in their deep learning models for skin cancer classification, although their approaches and implementations varied [105][106][107][108]. Omeroglu, Mohammed, Oral and Aydin [105] and Bao, Han, Huang and Muzahid [108] explicitly used soft attention mechanisms to enhance feature detection in skin lesions, Desale and Patil [107]'employed an attention mechanism in a convolutional capsule network, and Zhang, Wang, Cheng and Song [106]'s approach, while not explicitly stated as an attention mechanism, focused on capturing comprehensive image features, which can be seen as a form of attention to image details. Moreover, To, et al [109] introduced the MetaAttention model for skin lesion diagnosis.…”

Section: Attention To Skin Lesionsmentioning

confidence: 99%

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Alzahrani

2024

Preprint

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This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the complexities and imbalances inherent in skin lesions datasets. The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. SkinLiTE demonstrated superior performance across various metrics—accuracy, AUC , and F1 score—particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

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