CNN-Based Classification and Segmentation of Pancreatic Tumor Detection

Hafsa Mazhar; Kalsoom Safdar; Muhammad Usman Younus; Abdul Basit Dogar

doi:10.33411/IJIST/1997

Authors

Hafsa Mazhar Department of Computer Science & IT, University of Jhang, Jhang, Pakistan.
Kalsoom Safdar 1Department of Computer Science & IT, University of Jhang, Jhang, Pakistan. Faculty of Intelligent Computing, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia.
Muhammad Usman Younus Department of Computer Science and Information Technology, Baba Guru Nanak University, 39120 Nankana Sahib, Pakistan. Ecole Math´ematiques, Informatique, T´el´ecommunications de Toulouse, Universit´e de Toulouse, 31000 de Toulouse, France
Abdul Basit Dogar Department of Informatics and Systems, School of Systems and Technology, University of Management and Technology Lahore, Pakistan.

DOI:

https://doi.org/10.33411/IJIST/1997

Keywords:

Pancreatic Tumor, CNN, U-Net, Deep Learning, CT scan, Classification, PyTorch

Abstract

The tumors of the pancreas are linked with high mortality rates due to late discovery of the diseases, showing the need for timely and accurate diagnosis. The fire deep learning architecture, proposed in this research, has been shown to be a hybrid system for categorizing pancreatic tumors from computed tomography (CT) images. This involves training a U-Net to segment images and applying Convolutional Neural Networks (CNNs) to categorize computed tomography (CT) images. It is performed using a publicly available dataset of female pancreatic CT scans. Preprocessing was also performed for model generalization through normalization, resizing, and data augmentation. U-Net was used to perform pixel-wise segmentation of the tumor, yielding a Dice Coefficient of 0.89, i.e., very high segmentation accuracy. These results were then fed into a CNN classifier, which achieved 93 percent accuracy in determining whether a tumor is present. The results of the model training and testing procedure are presented. The effectiveness of the models is measured using quantitative performance metrics, including the Dice Coefficient, classification accuracy, precision, recall, F1-score, ROC curves, and confusion matrices. Accordingly, it has been executed by comparing a Python environment on Google Colab, using the PyTorch deep learning library, for pancreatic tumor detection.

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