Open Access

ARTICLE

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

R. Reena Roy^*, G. S. Anandha Mala

Department of Information Technology, Easwari Engineering College, Chennai, Tamilnadu, 600089, India

* Corresponding Author: R. Reena Roy. Email:

Computer Systems Science and Engineering 2022, 41(2), 677-688. https://doi.org/10.32604/csse.2022.016620

Received 06 January 2021; Accepted 15 June 2021; Issue published 25 October 2021

Abstract

Pancreatic cancer is one of the deadliest cancers, with less than 9% survival rates. Pancreatic Ductal Adeno Carcinoma (PDAC) is common with the general public affecting most people older than 45. Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages (advanced stage). One of the earlier symptoms of PDAC is Jaundice. Patients with diabetes, obesity, and alcohol consumption are also at higher risk of having pancreatic cancer. A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge. Features such as Mean Hue, Mean Saturation, Mean Value, and Mean Standard Deviation are computed after color space conversion from RGB to HSV. Fuzzy k-Nearest Neighbor (F-kNN) is designed for classification. The system proposed is trained and tested using features extracted from jaundiced eye images. The proposed system results indicate that this model can predict pancreatic cancer as earlier as possible, helping clinicians make better decisions for surgical planning.

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Keywords

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