2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 1 , PP: 248-260, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Eiman Talal Alharby 1 *
Doi: https://doi.org/10.54216/FPA.190119
Pancreatic cancer (PC) is an extremely malignant cancer type with a maximum rate of mortality. It remains a challenging form of tumor to treat due to its late analysis and aggressive nature, which drastically decreases the survival rate. Early analysis of PC is vital for enhancing the probabilities of treatment and survival. PC analysis was initially dependent upon imaging, and then the recent imaging offered a worse prognosis, restraining clinicians’ treatment choices. PC detection utilizing deep learning (DL) contains the application of advanced computational methods for analyzing medical image data like CT scans or MRI images, for the early and correct detection of PCs. DL approaches, particularly convolutional neural networks (CNNs), are trained on huge databases for diagnosing forms and anomalies indicative of PC. Therefore, this study presents a novel Coot Optimization Algorithm with Deep Learning based Medical Image Analysis for Pancreatic Cancer Diagnosis (COADL-MIAPCD) technique. The main objective of the COADL-MIAPCD approach is to proficiently examine the medical images for the detection of PC. The COADL-MIAPCD technique primarily applies a median filtering (MF) for image pre-processing. In addition, the COADL-MIAPCD approach allowed using of an improved SE-ResNet. Moreover, the COA has been utilized for the optimum parameter choice of the improved SE-ResNet. At last, the extreme learning machine (ELM) has been used for the recognition and classification of PCs. The simulation outcomes of the COADL-MIAPCD technique has been validated utilizing a medical image database. The obtained experimental values stated that COADL-MIAPCD technique achieves better performance than other models.
Pancreatic Cancer , Medical Image , Deep Learning , Coot Optimization Algorithm , Computed Tomography
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