The Role of Artificial Intelligence in Revolutionizing Cancer Detection and Diagnosis: A Focus on Convolutional Neural Networks

The integration of Artificial Intelligence (AI), particularly Convolutional Neural Networks (CNNs), has revolutionized cancer diagnostics by offering precise, efficient, and scalable solutions. CNNs excel at analysing complex patterns in medical imaging, enabling the detection of subtle anomalies critical for early-stage diagnosis. This paper provides a comprehensive review of CNN applications across imaging modalities such as mammograms, CT scans, MRIs, and histopathology slides, highlighting their adaptability and accuracy. Key topics include advancements in CNN architectures like ResNet, DenseNet, and InceptionNet, along with performance metrics such as sensitivity and specificity. Challenges such as limited annotated datasets, computational demands, and the “black-box” nature of CNNs are addressed, emphasizing the need for data augmentation, transfer learning, and explainable AI to foster clinical acceptance. Pre-processing techniques and transfer learning are explored as vital tools to enhance model performance despite resource constraints. Finally, the paper identifies emerging trends and proposes strategies, including multimodal data integration and enhanced interpretability, to address current limitations. These advancements aim to improve the scalability and real-world application of CNNs in cancer diagnostics, driving progress in early detection, treatment planning, and overall patient outcomes.