利用卷积神经网络 (CNN) 的深度学习在 PC 支持的医学研究中最先进的程序中脱颖而出。本文提出的方法包括两个关键阶段。在第一阶段，所提出的深度顺序 CNN 模型对图像进行预处理，以从皮肤病变中分离出感兴趣的区域并提取特征，捕获相关模式并检测多个病变。第二阶段采用网络工具，通过承诺患者健康诊断来增加模型的可视化。所提出的模型利用与 HAM 10,000 数据集相关的数据库进行了彻底的训练、验证和测试。该模型在皮肤病变分类方面的准确率达到 96.25%，表现出显着的强度区域。通过评估方法和用户反馈验证的所提出的模型所取得的结果表明，相对于当前最先进的皮肤病变分类（恶性/良性）方法有实质性的改进。与其他模型相比，顺序 CNN 超越了 CNN 迁移学习 (87.9%)、VGG 19 (86%)、ResNet-50 VGG-16 (94.14%)、Inception v3 (90%)、Vision Transformers（RGB 图像）（92.14） %) 和熵-NDOELM 方法 (95.7%)。研究结果证明了深度学习、卷积神经网络和顺序 CNN 在疾病检测和分类方面的潜力，最终彻底改变黑色素瘤检测，从而提高患者的考虑。

Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign). In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%). The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration.