Cancer Detection Techniques: An Overview of Traditional and AI-Based Methods and Their Comparative Analysis

Cancer affects millions of individuals globally, making it a serious public health issue. Early identification of cancer can significantly improve patient survival outcomes. Machine learning (ML) along with deep learning (DL) holds potential for doing so. This publication summarizes the latest findings on ML and DL-based cancer detection, with a focus on breast, lung, prostate, and colon cancer. The many structures and algorithms used in cancer detection are presented, along with the data sources used, such as radiographic images, genomic data, and electronic health records. The constraints and difficulties associated with machine and deep learning-based cancer diagnosis are highlighted, including the need for extensive and diverse datasets, the interpretability of models, and the possibility of bias. The creation of explainable artificial intelligence [AI] models along with other diagnostic methods is among the future research goals that are finally presented. These initiatives aim to increase the precision of cancer detection and patient outcomes.