标题： 使用计算机视觉进行孟加拉国皮肤疾病诊断 提升深度学习模型在皮肤癌分类中的可解释性和透明度 显示英文标题

标题： Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification

摘要： 每年发现超过200万新病例，皮肤癌是全球最普遍的癌症类型，在孟加拉国则是仅次于乳腺癌的第二常见癌症。 早期检测和治疗对于改善患者预后至关重要；然而，孟加拉国缺乏足够数量的皮肤科医生和能够诊断和治疗皮肤癌的合格医务人员。 因此，许多病例仅在晚期才被诊断出来。 研究表明，深度学习算法可以有效地对皮肤癌图像进行分类。 然而，这些模型通常缺乏可解释性，使得难以理解它们的决策过程。 这种不透明性阻碍了深度学习在提高皮肤癌检测和治疗中的应用。 在本文中，我们提出了一种方法，旨在提高孟加拉国皮肤癌分类中深度学习模型的可解释性。 我们的技术结合了显著性图和注意力图，以可视化影响模型诊断的关键特征。 显示英文摘要

摘要： With over 2 million new cases identified annually, skin cancer is the most prevalent type of cancer globally and the second most common in Bangladesh, following breast cancer. Early detection and treatment are crucial for enhancing patient outcomes; however, Bangladesh faces a shortage of dermatologists and qualified medical professionals capable of diagnosing and treating skin cancer. As a result, many cases are diagnosed only at advanced stages. Research indicates that deep learning algorithms can effectively classify skin cancer images. However, these models typically lack interpretability, making it challenging to understand their decision-making processes. This lack of clarity poses barriers to utilizing deep learning in improving skin cancer detection and treatment. In this article, we present a method aimed at enhancing the interpretability of deep learning models for skin cancer classification in Bangladesh. Our technique employs a combination of saliency maps and attention maps to visualize critical features influencing the model's diagnoses.