Open Access

ARTICLE

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Areej A. Malibari¹, Reem Alshahrani², Fahd N. Al-Wesabi^3,*, Siwar Ben Haj Hassine³, Mimouna Abdullah Alkhonaini⁴, Anwer Mustafa Hilal⁵

1 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
4 Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Fahd N. Al-Wesabi. Email:

Computers, Materials & Continua 2022, 72(2), 3799-3813. https://doi.org/10.32604/cmc.2022.026131

Received 16 December 2021; Accepted 22 February 2022; Issue published 29 March 2022

Abstract

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the class labels are allotted to the input MRI images. Moreover, the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm (KHA) which results in improved classification performance. In order to demonstrate the good classification outcome of the DTL-PSCC technique, a wide range of simulations take place on benchmark MRI datasets. The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

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