Advances in Image Processing and Pattern Recognition in Cancer Detection, Prediction, Diagnosis, and Prognosis

Cancer remains a global health issue, with early detection, correct diagnosis, and exact prognosis playing critical roles in improving patient outcomes. Recent developments in image processing and pattern recognition have significantly enhanced cancer detection, prediction, diagnosis, and prognosis, bridging the gap between conventional radiological methods and state-of-the-art artificial intelligence (AI)-based analytics. The abstract provides an in-depth introduction of the concepts behind, developing methods, and practical applications of image processing and pattern recognition in oncology. Image preprocessing algorithms, tumor segmentation algorithms, feature extraction, and pattern recognition methods based on artificial intelligence such as machine learning and deep learning models (e.g., CNNs, RNNs, and transformers) are all significant areas. We study cancer prediction using radiomics and radio genomics, artificial intelligence-based histopathological diagnosis, and fusion of multimodal data from imaging, genomics, and clinical history for personalized prognosis. The impact of explainable AI, 3D/4D imaging, nanotechnology-facilitated imaging, and cloud-based AI solutions is also discussed, including precision oncology and remote diagnosis. Even with these developments, significant clinical use limitations remain, such as poor annotated datasets, lack of interpretability, ethical considerations, and regulatory issues. The abstract proceeds with focusing on emerging trends, including federated learning, quantum computing, and real-time AI applications, that can potentially revolutionize cancer imaging and management. Image processing and pattern recognition, if integrated with interdisciplinary research, have the potential to develop more precise, equitable, and egalitarian cancer care in the future.