Application of Deep Learning on Pancreas Tumor Detection

Authors : RUTATINA RUTAGONYA Frank; Dr. Wilson Musoni

Volume/Issue : Volume 8 - 2023, Issue 10 - October

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This study focuses on the importance of detecting and diagnosing pancreatic tumors accurately to improve patient outcomes. It explores the use of deep learning algorithms, specifically convolutional neural networks, for automated pancreas tumor detection using CT scans. The CT images undergo preprocessing steps such as noise reduction, normalization, and image resampling. These preprocessed images are then used to train a deep learning model that learns the characteristics of pancreatic tumors. The model is trained using a large dataset of annotated CT images, consisting of both tumor-positive and tumor-negative cases. Various optimization techniques and loss functions are employed to maximize the model's performance. The initial results show promising outcomes, with the model achieving high accuracy in pancreas tumor detection. Its sensitivity and specificity are evaluated to assess its ability to correctly identify tumor presence or absence. The model's performance is further validated using independent testing datasets to ensure its generalizability. The study aims to develop an efficient and reliable automated system for detecting pancreatic tumors by leveraging deep learning techniques on CT images. This approach has the potential to assist radiologists and clinicians in early and accurate diagnosis of pancreatic cancer, leading to timely treatment interventions and improved patient outcomes.


Paper Submission Last Date
31 - December - 2023

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