Early detection is crucial for successful cancer treatment and increasing survivability rates, particularly in the most common forms. Ten different cancers have been identified in most of these advances that effectively use CNNs (Convolutional Neural Networks) for classification. The distinct architectures of CNNs used in each study concentrate on pattern recognition for different types of cancer across various datasets. The advantages and disadvantages of each approach are identified by comparing these architectures. This study explores the potential of integrating CNNs into clinical practice to complement traditional diagnostic methods. It also identifies the top-performing CNN architectures, highlighting their role in enhancing diagnostic capabilities in healthcare.

翻译：早期检测对于成功治疗癌症和提高生存率至关重要，尤其是针对最常见癌症类型。在大多数相关进展中已识别出十种不同癌症，这些进展有效利用卷积神经网络（CNN）进行分类。每项研究中使用的不同CNN架构专注于针对不同数据集中的各类癌症进行模式识别。通过比较这些架构，明确了每种方法的优缺点。本研究探讨了将CNN整合到临床实践中以补充传统诊断方法的潜力，并指出了表现最佳的CNN架构，强调了它们在提升医疗诊断能力方面的作用。