深度学习
前列腺癌
人工智能
卷积神经网络
Python(编程语言)
计算机科学
机器学习
医学影像学
软件
癌症
模式识别(心理学)
医学
内科学
程序设计语言
操作系统
作者
C. Chun,Naeem Akhtar,Shaymaa Mohammed Abdulameer,Salama A. Mostafa,Abdulkareem A. Hezam
出处
期刊:JOURNAL OF SOFT COMPUTING AND DATA MINING
[Penerbit UTHM]
日期:2022-08-08
卷期号:3 (2)
标识
DOI:10.30880/jscdm.2022.03.02.001
摘要
According to medical data, prostate cancer has been one of the most lethal malignancies in recent years. Early detection of prostate cancer significantly influences the tumor's treatability. Image analysis software that operates using a machine learning or deep learning algorithm is one of the techniques utilized to aid in the early and rapid identification of prostate cancer. This paper evaluates the performance of three deep learning Convolutional neural network (CNN) algorithms in detecting prostate cancer. Using Python, three deep learning models, ResNet50, InceptionV3, and VGG16, are subsequently created on the Kaggle platform. These three models have been applied to various medical image diagnostic problems and have won several contests. This study used 620image samples from the Cancer Imaging Archive (TCIA) data source. Accuracy, f1 score, recall, and precision are used to evaluate the performance of the three models. The extracted test results indicate that the VGG16 achieves the highestlevel of accuracy at 95.56percent, followed by the ResNet50 at 86.67percent and the InceptionV3 at 85.56percent.
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