Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Sensors

Muhammad Adnan Khan¹  Sheeraz Akram²  Iftikhar Naseer²  Arfan Jaffar²  Tehreem Masood²  Amir Mosavi³ 

[1] Department of Software, Gachon University, Seongnam 13120, Korea;Faculty of Computer Science & Information Technology, The Superior University, Lahore 54600, Pakistan;John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary;

关键词: LeNet;    AlexNet;    deep learning;    LUNA16;    machine learning;    artificial intelligence;   

DOI  :  10.3390/s22124426

来源: DOAJ

【 摘 要 】

The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.

【 授权许可】

Unknown