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. 2024 Oct 31;24(21):7005.
doi: 10.3390/s24217005.

Diagnosis of Pancreatic Ductal Adenocarcinoma Using Deep Learning

Fulya Kavak  1 , Sebnem Bora  1 , Aylin Kantarci  1 , Aybars Uğur  1 , Sumru Cagaptay  2 , Deniz Gokcay  2 , Anıl Aysal  2 , Burcin Pehlivanoglu  2 , Ozgul Sagol  2
Affiliations

Diagnosis of Pancreatic Ductal Adenocarcinoma Using Deep Learning

Fulya Kavak et al. Sensors (Basel). .

Abstract

Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across various domains, including health care. There is a significant effort to use AI for the early diagnosis and detection of diseases, offering cost-effective and timely solutions to enhance patient outcomes. This study introduces a deep learning network aimed at analyzing pathology images for the accurate diagnosis of pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC). Utilizing a novel dataset comprised of cases diagnosed with PDAC and/or chronic pancreatitis, this study applies deep learning algorithms to assess the effectiveness and reliability of the diagnostic process. The dataset was enhanced through image duplication and the creation of a second dataset with varied dimensions, facilitating the training of advanced transfer learning models including InceptionV3, DenseNet, ResNet, VGG, EfficientNet, and a specially designed deep neural network. The study presents a convolutional neural network model, optimized for the rapid and accurate detection of pancreatic cancer, and conducts a comparative analysis with other models to select the most accurate algorithm for a decision support system. The results from Dataset 1 show that EfficientNetB0 achieved a high success rate of 92%. In Dataset 2, VGG16 was found to have high performance, with a success rate of 92%. On the other hand, ResNet50 achieved a remarkable success rate of 96% despite a moderate training time and showed high precision, recall, F1 score, and accuracy. These results provide valuable data to demonstrate and share the relevance of different deep learning models in pancreatic cancer diagnosis.

Keywords: classification; convolutional neural networks; deep learning; health services; pancreatic ductal adenocarcinoma; pathology images.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
(a) A CP image showing the features of the dataset, and (b) a PDAC image showing different disease data. Both of these images are part of the datasets that were used in the study.
Figure 2
Figure 2
(a) PDAC/CP image; (b) the illustration demonstrates the selection of normalized tissue patches based on the CNN classification; (c) selected images used after the data cleanup.
Figure 3
Figure 3
(a–d) are the images obtained as a result of image enhancement.
Figure 4
Figure 4
Design of the CNN architecture.
Figure 5
Figure 5
Confusion matrix.
Figure 6
Figure 6
Training time of the learning models for Dataset 1.
Figure 7
Figure 7
Training time of the learning models for Dataset 2.
Figure 8
Figure 8
A comparison of the accuracy value of deep learning algorithms.
See this image and copyright information in PMC

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