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Abstract

Artificial Neural Networks (ANNs) have revolutionized the landscape of medical diagnostics, particularly in oncology, where early and accurate detection can substantially impact patient outcomes. This paper provides a comprehensive review of the role of ANNs in precision oncology, emphasizing cancer detection, classification, and prognosis modeling. With the surge in multi-omics data, imaging modalities, and electronic health records, traditional diagnostic methods are being supplemented and sometimes replaced by data-driven approaches. ANNs, owing to their capacity for learning complex patterns, have demonstrated superior performance in tasks such as tumor identification in histopathological images, genomic data classification, and survival prediction. This review highlights key ANN architectures including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and hybrid models, discussing their specific applications and performance metrics across various cancer types such as breast, lung, colorectal, and prostate cancers. The paper also explores challenges in model interpretability, data heterogeneity, and clinical integration while presenting recent advancements in explainable AI, federated learning, and transfer learning as potential solutions. A critical evaluation of publicly available datasets and the importance of cross-institutional collaborations is discussed to ensure the scalability and robustness of ANN-based solutions. By consolidating findings from recent literature, this review offers a roadmap for future research and implementation strategies in ANN-powered oncology.