The performance of a classifier depends on the tuning of its parame ters. In this paper, we have experimented the impact of various tuning parameters on the performance of a deep convolutional neural network (DCNN). In the ex perimental evaluation, we have considered a DCNN classifier that consists of 2 convolutional layers (CL), 2 pooling layers (PL), 1 dropout, and a dense layer. To observe the impact of pooling, activation function, and optimizer tuning pa rameters, we utilized a crack image dataset having two classes: negative and pos itive. The experimental results demonstrate that with the maxpooling, the DCNN demonstrates its better performance for adam optimizer and tanh activation func tion.
@article{arxiv.2506.03184,
title = {Impact of Tuning Parameters in Deep Convolutional Neural Network Using a Crack Image Dataset},
author = {Mahe Zabin and Ho-Jin Choi and Md. Monirul Islam and Jia Uddin},
journal= {arXiv preprint arXiv:2506.03184},
year = {2025}
}
Comments
8 pages, 2 figures, published at Proceedings of the 15th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE 2021), Jeju, Repubilc of Korea