PDF(Pubmed)
Abstract：
Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis. Leveraging the HAM10000 dataset, a comprehensive collection of dermatoscopic images encompassing a diverse range of skin lesions, this study introduces a sophisticated CNN model tailored for the nuanced task of skin lesion classification. The model\'s architecture is intricately designed with multiple convolutional, pooling, and dense layers, aimed at capturing the complex visual features of skin lesions. To address the challenge of class imbalance within the dataset, an innovative data augmentation strategy is employed, ensuring a balanced representation of each lesion category during training. Furthermore, this study introduces a CNN model with optimized layer configuration and data augmentation, significantly boosting diagnostic precision in skin cancer detection. The model\'s learning process is optimized using the Adam optimizer, with parameters fine-tuned over 50 epochs and a batch size of 128 to enhance the model\'s ability to discern subtle patterns in the image data. A Model Checkpoint callback ensures the preservation of the best model iteration for future use. The proposed model demonstrates an accuracy of 97.78% with a notable precision of 97.9%, recall of 97.9%, and an F2 score of 97.8%, underscoring its potential as a robust tool in the early detection and classification of skin cancer, thereby supporting clinical decision-making and contributing to improved patient outcomes in dermatology.
摘要：
皮肤癌是肿瘤学最重要的挑战之一，它的早期发现对于成功的治疗结果至关重要。传统的诊断方法依赖于皮肤科医生的专业知识，创造对更可靠的需求，自动化工具。本研究探索深度学习，特别是卷积神经网络(CNN)，提高皮肤癌诊断的准确性和效率。利用HAM10000数据集，全面收集皮肤镜检查图像，涵盖各种皮肤病变，这项研究引入了一个复杂的CNN模型，为皮肤病变分类的细微任务量身定制。该模型的体系结构是复杂的设计与多个卷积，池化,和致密的层，旨在捕捉皮肤病变的复杂视觉特征。为了解决数据集中的类不平衡的挑战，采用了创新的数据增强策略，确保在训练期间每个病变类别的平衡表示。此外，本研究引入了一种具有优化的层配置和数据增强的CNN模型，显着提高皮肤癌检测的诊断精度。使用Adam优化器优化模型的学习过程，参数微调超过50个时期和128的批量大小，以增强模型的能力，以辨别图像数据中的微妙模式。模型检查点回调可确保保留最佳模型迭代以供将来使用。所提出的模型具有97.78%的精度，显著的精度为97.9%，召回97.9%，F2得分为97.8%,强调其作为皮肤癌早期检测和分类的强大工具的潜力，从而支持临床决策，并有助于改善皮肤科患者的预后。
