Open Access

ARTICLE

A Pooling Method Developed for Use in Convolutional Neural Networks

İsmail Akgül^*

Departmant of Computer Engineering, Faculty of Engineering and Architecture, Erzincan Binali Yıldırım University, Erzincan, 24002, Türkiye

* Corresponding Author: İsmail Akgül. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)

Computer Modeling in Engineering & Sciences 2024, 141(1), 751-770. https://doi.org/10.32604/cmes.2024.052549

Received 05 April 2024; Accepted 19 June 2024; Issue published 20 August 2024

Abstract

In convolutional neural networks, pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models. These methods reduce the computational amount of convolutional neural networks, making the neural network more efficient. Maximum pooling, average pooling, and minimum pooling methods are generally used in convolutional neural networks. However, these pooling methods are not suitable for all datasets used in neural network applications. In this study, a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks. This method, which we call MAM (Maximum Average Minimum) pooling, is more interactive than other traditional maximum pooling, average pooling, and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value. The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks. To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods, training was carried out on the LeNet-5 model using CIFAR-10, CIFAR-100, and MNIST datasets. According to the results obtained, the proposed MAM pooling method performed better than the maximum pooling, average pooling, and minimum pooling methods in all pool sizes on three different datasets.

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