AI Enhanced Medical Image Analysis for Early Pancreatic Cancer Detection

Abstract

Pancreatic cancer is among the most lethal malignancies worldwide, primarily due to its frequent diagnosis at advanced stages, which contributes to a notably low five-year survival rate. The organ’s deep anatomical position within the abdominal cavity, often concealed by surrounding structures, poses significant challenges for early clinical detection through conventional examinations. However, early identification is achievable with advanced medical imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI). Recent developments in Computer-Aided Diagnosis (CAD) systems have demonstrated encouraging potential in enhancing the detection of pancreatic cancer at an earlier stage. This study introduces a comprehensive framework that extends current CAD methodologies by incorporating five integrated components: image preprocessing, segmentation, feature extraction, classification, and explainable artificial intelligence (XAI). The preprocessing stage enhances image clarity using color transformation and isotropic diffusion filtering. For segmentation, a U-Net-based neural architecture effectively delineates tumor regions. Subsequent feature extraction, performed using a ResNet-50 model, captures key image attributes such as contrast, correlation, and dissimilarity. A hybrid classification model, combining Deep Convolutional Neural Networks (DCNN) and Deep Belief Networks (DBN), is employed to distinguish between malignant and benign tissues. To promote transparency and clinical acceptance, interpretability tools like Grad-CAM and SHAP are integrated, offering visual and statistical insights into model decision-making.

Introduction

1. Background on Pancreatic Cancer (PC):

• High Mortality: Pancreatic cancer is one of the deadliest cancers globally due to late detection and limited treatment options.

• Late Diagnosis: About 80% of cases are detected at an advanced, non-surgical stage due to asymptomatic progression.

• Risk Factors: Include modifiable (smoking, pancreatitis, diabetes) and non-modifiable (age, gender, ethnicity) elements.

• Biological Basis: Cancer arises from uncontrolled cell growth, leading to tumors and metastasis that impair pancreas function.

2. Role of Imaging in PC Diagnosis:

• CT Scans: Most common imaging modality, offering 76–96% sensitivity, especially for larger tumors.

• Challenge: Accurate segmentation of the pancreas is difficult due to low contrast and complex anatomy.

3. Proposed Solution: AI-Powered CAD System

A five-stage hybrid framework is proposed for automatic detection and classification of PC in CT images:

A. Preprocessing:

• Converts RGB CT images to greyscale.

• Applies isotropic diffusion filtering to reduce noise and preserve anatomical boundaries.

B. Segmentation:

• Uses U-Net2D architecture to isolate the pancreas and detect tumors.

• Dice Loss is used for training to maximize overlap between predicted and ground-truth segmentation.

C. Feature Extraction:

• ResNet-50 extracts features like texture, contrast, and correlation from segmented regions.

• Uses GLCM-based features and histogram-based thresholding for added statistical insights.

• A synergic signal network refines learning by comparing similar/dissimilar regions.

D. Classification:

• Combines Deep Convolutional Neural Network (DCNN) with Deep Belief Network (DBN) to capture both spatial patterns and high-level features.

• DCNN handles image-based spatial patterns, while DBN captures abstract probabilistic features.

• Trained using contrastive divergence and convolutional operations.

E. Explainable AI (XAI):

• Grad-CAM highlights influential image areas contributing to predictions (visual explanation).

• SHAP assigns importance scores to features (statistical explanation).

• Improves clinical interpretability and trust in AI decisions.

4. Dataset:

• Source: The Cancer Imaging Archive (NIH Clinical Centre).

• Composition: 82 contrast-enhanced 3D abdominal CT scans from 80 patients (53 male, 27 female, aged 18–76).

• Ground Truth: Manual segmentation by medical student and validated by senior radiologist.

5. Results:

Segmentation:

• U-Net2D model achieved high accuracy in delineating pancreas regions.

• Outperformed conventional methods, especially in low-contrast areas.

Classification:

• Recall: 0.75 (sensitive to cancer cases).

• Precision: 0.48.

• Accuracy: 62%.

• F1 Score: Balanced across cancerous and non-cancerous cases.

• The model minimized false negatives, which is crucial in cancer diagnosis.

Conclusion

This study introduces a comprehensive AI-driven framework aimed at facilitating the early detection of pancreatic cancer by combining advanced image preprocessing, deep learning-based segmentation, hybrid feature extraction and classification, along with explainable AI methodologies [3][9][2][7]. The early diagnosis of pancreatic cancer remains a substantial clinical hurdle due to its asymptomatic nature and anatomically concealed location within the body [1][13]. To overcome this challenge, the proposed pipeline begins with a robust preprocessing phase involving color conversion and isotropic diffusion filtering [9]. These methods significantly enhance image quality by reducing noise and preserving anatomical boundaries, thereby improving contrast and making subtle pathological features more detectable in CT images. The segmentation component employs the U-Net2D architecture, a well-established model in biomedical image analysis, noted for its encoder-decoder structure and fine spatial resolution capabilities [2]. This architecture enables precise delineation of pancreatic tissue and lesions, even in low-contrast or morphologically complex scans. Effective segmentation minimizes extraneous data and ensures that only clinically relevant areas are carried forward for classification—an essential step for improving diagnostic accuracy and reducing both false positives and false negatives in high-stakes scenarios such as cancer detection. Feature extraction is performed using ResNet-50, a deep residual network adept at capturing intricate texture features, shapes, and contextual image patterns [3]. Its residual learning framework allows for the training of deeper architectures without degradation, making it ideal for handling high-dimensional medical data. These extracted features serve as inputs to a hybrid classification system that integrates the capabilities of Deep Convolutional Neural Networks (DCNN) and Deep Belief Networks (DBN) [2][12][8]. While DCNN effectively captures spatial dependencies, the DBN component contributes by learning abstract and probabilistic representations. This hybrid model enhances overall classification performance, demonstrates resilience to noise, and generalizes well across varied datasets. To ensure clinical interpretability and promote trust in AI-driven decisions, Explainable AI (XAI) tools such as Grad-CAM and SHAP are incorporated into the decision pipeline [7][5]. Grad-CAM facilitates the visual localization of critical regions by generating class-specific heatmaps, allowing clinicians to verify that the model focuses on diagnostically relevant areas. SHAP, on the other hand, provides feature-level attribution scores, quantifying each input variable\'s contribution to the final prediction based on game-theoretic principles [20]. This combined approach addresses the opaque nature of deep learning models, enhancing transparency and clinician confidence, and supporting potential integration into clinical workflows. In summary, the proposed system offers a holistic, interpretable, and technically robust approach to early pancreatic cancer detection by combining precise segmentation, deep feature learning, hybrid classification strategies, and strong model explainability [3][9][2][7]. Beyond improving diagnostic accuracy, the system is well-aligned with real-world clinical needs through its emphasis on transparency and usability. Future developments may include validation on larger, multi-institutional datasets, the incorporation of multimodal data such as genomic profiles or patient history [4][11], and the deployment of the model within clinical decision support systems [7]. The methodologies and findings presented here represent a significant step forward in the evolution of intelligent and interpretable diagnostic technologies.

References

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Copyright

Copyright © 2025 Nujha N, Priyadharshini R, Raj Laxmi S N, Roshika P, Sumithra R P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.