Artificial intelligence (AI) approaches have been shown to be effective in classifying skin diseases and outperforming dermatologists in diagnosis. Using big data as a dermatological diagnosis service can present several challenges. One challenge is the need to accurately label and classify large amounts of data, such as images of infected skin. This can be time-consuming and resource-intensive. It is important to implement proper safeguards to protect sensitive medical information. Despite these challenges, the use of big data in dermatology can lead to several outcomes. It can improve the accuracy of diagnoses, leading to better patient outcomes. It can also help to identify patterns and trends in skin conditions, allowing for earlier detection and prevention. In addition, big data can be used to identify risk factors for certain conditions, enabling targeted preventative measures. Convolutional neural networks (CNNs) have been widely used for skin lesion classification, and recent advances in machine learning algorithms have led to a decrease in misclassification rates compared to manual categorization by dermatologists. This article utilizes the use of big data for accurate dermatological diagnosis services, it introduces the use of various CNNs for classifying different types of skin cancer. While deep learning and pretrained transfer learning techniques have advantages over traditional methods, they also have limitations and the potential for incorrect identification under certain circumstances. This work discusses these vulnerabilities and employs pretrained models to classify 11 different skin diseases, including monkey pox. The performance of the system is evaluated using accuracy, loss, precision, recall, and F1 score, and the results show that the system is able to diagnose the 11 skin illnesses with an accuracy rate of 97% using transfer learning in Keras and computer vision models. The best model was found to be Inception_ResNetV2 with 50 epochs and the Adam optimizer.

人工智能 (AI) 方法已被证明可以有效对皮肤病进行分类，并且在诊断方面优于皮肤科医生。使用大数据作为皮肤病诊断服务可能会带来一些挑战。一项挑战是需要准确地标记和分类大量数据，例如受感染皮肤的图像。这可能非常耗时且耗费资源。实施适当的保护措施来保护敏感的医疗信息非常重要。尽管存在这些挑战，但在皮肤病学中使用大数据可以带来多种结果。它可以提高诊断的准确性，从而改善患者的治疗效果。它还可以帮助识别皮肤状况的模式和趋势，以便及早发现和预防。此外，大数据还可用于识别某些情况下的风险因素，从而采取有针对性的预防措施。卷积神经网络（CNN）已广泛用于皮肤病变分类，与皮肤科医生的手动分类相比，机器学习算法的最新进展导致错误分类率降低。本文利用大数据提供准确的皮肤病诊断服务，介绍了使用各种 CNN 对不同类型的皮肤癌进行分类。虽然深度学习和预训练迁移学习技术比传统方法具有优势，但它们也有局限性，并且在某些情况下可能会出现错误识别。这项工作讨论了这些漏洞，并采用预训练模型对 11 种不同的皮肤病进行分类，包括猴痘。使用准确度、损失、精确度、召回率和 F1 分数来评估系统的性能，结果表明，使用 Keras 中的迁移学习和计算机视觉，系统能够以 97% 的准确率诊断 11 种皮肤疾病楷模。最佳模型是具有 50 个 epoch 和 Adam 优化器的 Inception_ResNetV2。