Application of Artificial Intelligence in Medical Imaging for Early Cancer Detection - Ahmed Ayad Abdulsattar; Zainab Kreemm Hadi Jassim; Sajjad Mohammed Salman; Hussein Jabbar Saleh; Ahmed Isam Hussein (American Journal of Botany and Bioengineering, 2025)
Early detection of cancer significantly improves treatment outcomes, yet traditional diagnostic methods often fall short due to the complexity of medical imaging data and human interpretive limitations. To address this gap, this study investigates the application of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), in automating and enhancing the detection of cancer through medical imaging modalities such as MRI, CT, X-ray, and ultrasound. Utilizing supervised learning and convolutional neural networks, the proposed models analyze high-resolution images to identify patterns, classify cancer stages, and support clinical decision-making. Findings demonstrate that AI models outperform traditional techniques in accuracy, sensitivity, and early-stage detection, with success rates exceeding 90% in certain imaging modalities. The results underscore AI’s transformative potential in healthcare by enabling timely diagnosis, reducing radiologist workload, and supporting personalized treatment planning.