Artificial Intelligence–Driven Medical Image Processing for Early Cancer Diagnosis and Clinical Decision Support

Abstract

Cancer remains one of the leading causes of mortality worldwide, and early diagnosis plays a crucial role in improving treatment outcomes and patient survival. Recent advances in artificial intelligence (AI) and medical image processing have transformed computer-aided diagnosis by enabling automated detection, segmentation, and classification of cancerous lesions from medical imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), mammography, histopathology, ultrasound, and positron emission tomography (PET). Deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable capability in extracting discriminative imaging features while reducing diagnostic variability associated with manual interpretation. This study presents an AI-driven medical image processing framework for early cancer diagnosis and clinical decision support. The framework integrates image preprocessing, lesion segmentation, deep feature extraction, classification, and decision support into a unified pipeline designed to improve diagnostic efficiency and accuracy. In addition, the study discusses the challenges associated with data heterogeneity, model interpretability, limited annotated datasets, and clinical deployment. The proposed framework highlights the potential of AI-assisted imaging systems to support radiologists and clinicians by facilitating early cancer detection, reducing diagnostic workload, and enabling precision medicine.