Comparative analysis of the related work.

Over the past few decades, skin cancer has emerged as a major global health concern. The efficacy of skin cancer treatment greatly depends upon early diagnosis and effective treatment. The automated classification of Melanoma and Nonmelanoma is quite challenging task due to presence of high visual similarities across different classes and variabilities within each class. According to the best of our knowledge, this study represents the classification of Melanoma and Nonmelanoma utilising Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) under the Nonmelanoma class for the first time. Therefore, this research focuses on automated detection of different skin cancer types to provide assistance to the dermatologists in timely diagnosis and treatment of Melanoma and Nonmelanoma patients. Recently, artificial intelligence (AI) methods have gained popularity where Convolutional Neural Networks (CNNs) are employed to accurately classify various skin diseases. However, CNN has limitation in its ability to capture global contextual information which may lead to missing important information. In order to address this issue, this research explores the outlook attention mechanism inspired by vision outlooker, which improves important features while suppressing noisy features. The proposed SkinViT architecture integrates an outlooker block, transformer block and MLP head block to efficiently capture both fine level and global features in order to enhance the accuracy of Melanoma and Nonmelanoma classification. The proposed SkinViT method is assessed by different performance metrics such as recall, precision, classification accuracy, and F1 score. We performed extensive experiments on three datasets, Dataset1 which is extracted from ISIC2019, Dataset2 collected from various online dermatological database and Dataset3 combines both datasets. The proposed SkinViT achieved 0.9109 accuracy on Dataset1, 0.8911 accuracy on Dataset3 and 0.8611 accuracy on Dataset2. Moreover, the proposed SkinViT method outperformed other SOTA models and displayed higher accuracy compared to the previous work in the literature. The proposed method demonstrated higher performance efficiency in classification of Melanoma and Nonmelanoma dermoscopic images. This work is expected to inspire further research in implementing a system for detecting skin cancer that can assist dermatologists in timely diagnosing Melanoma and Nonmelanoma patients.

近数十年来，皮肤癌已成为全球主要的公共卫生关切问题。皮肤癌的治疗效果极大依赖于早期诊断与有效治疗。黑色素瘤（Melanoma）与非黑色素瘤（Nonmelanoma）的自动化分类是一项极具挑战性的任务，原因在于不同类别间视觉相似度较高，且同一类别内部存在显著变异。据我们所知，本研究首次在非黑色素瘤类别下纳入基底细胞癌（Basal Cell Carcinoma, BCC）与鳞状细胞癌（Squamous Cell Carcinoma, SCC）开展分类研究。因此，本研究聚焦于不同皮肤癌类型的自动化检测，旨在为皮肤科医生及时诊断与治疗黑色素瘤及非黑色素瘤患者提供辅助支持。

近年来，人工智能（AI）方法日益普及，卷积神经网络（Convolutional Neural Networks, CNNs）被广泛应用于多种皮肤疾病的精准分类。然而，卷积神经网络在捕获全局上下文信息方面存在局限，可能导致重要特征信息遗漏。为解决这一问题，本研究探索了受视觉展望器（vision outlooker）启发的展望注意力机制，该机制可强化关键特征并抑制噪声特征。

本研究提出的SkinViT架构整合了展望器模块、Transformer模块与MLP头模块，能够高效捕获细粒度特征与全局特征，进而提升黑色素瘤与非黑色素瘤分类的准确率。我们通过召回率、精确率、分类准确率及F1分数等多项性能指标对所提SkinViT方法进行评估。我们在三个数据集上开展了大量实验：数据集1取自ISIC2019，数据集2采集自多个在线皮肤病学数据库，数据集3则整合了前述两个数据集。所提SkinViT在数据集1上的准确率达0.9109，在数据集3上达0.8911，在数据集2上达0.8611。此外，所提SkinViT方法优于其他当前最优（SOTA）模型，相较于现有文献中的前期研究，展现出更高的分类准确率。本方法在黑色素瘤与非黑色素瘤皮肤镜图像的分类任务中表现出更优异的性能效率。本研究有望推动相关领域的进一步研究，开发可协助皮肤科医生及时诊断黑色素瘤与非黑色素瘤患者的皮肤癌检测系统。