Pancreatic cancer is one of the most aggressive and lethal forms of cancer, with early detection and accurate grading being crucial for effective treatment planning and improving patient outcomes. Recent advancements in artificial intelligence (AI) have shown promise in enhancing the diagnostic accuracy of medical imaging for pancreatic cancer detection and classification. However, most of the existing AI models focus on imaging modalities such as MRI, CT, and PET scans, leaving a significant gap in utilizing pathological images for cancer grading. This survey provides a comprehensive review of the current state-of-the-art AI models developed for pancreatic cancer detection, highlighting the use of deep learning and ensemble learning approaches on MGG (May-Grünwald-Giemsa) and H&E (Haematoxylin and Eosin) stained pathological images. It discusses the effectiveness of binary and multiclass classification models, the application of transfer learning for feature extraction, and the integration of nature-inspired optimization techniques for feature engineering. The survey identifies key research gaps, including the lack of focus on pancreatic cancer grading using pathological images, and suggests future directions for developing AI-based grading systems that leverage high-resolution pathological images. The proposed methodologies aim to bridge the gap by developing robust, accurate, and clinically applicable AI models, which could significantly enhance the diagnostic and prognostic capabilities in clinical oncology.

pancreatic cancer, artificial intelligence, deep learning, ensemble learning, transfer learning, pathological images, MGG stained images, H&E-stained images, cancer grading, feature extraction.