A Comparison of Pooling Methods for Convolutional Neural Networks

Zafar, Afia; Aamir, Muhammad; Nawi, Nazri Mohd; Ali, Arshad; Riaz, Saman; Alruban, Abdulrahman; Dutta, Ashit Kumar; Almotairi, Sultan

doi:10.3390/app12178643

Cited by 120 publications

(49 citation statements)

References 72 publications

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“…Mainly, it is always 2 × 2 pixels applied with a 2 pixels stride. It means that pooling layer always going to reduce the size by a factor of 2 of each feature map [ 78 ]. Two of the common functions used in pooling operation are given below: Average Pooling A convolutional neural network’s layers repeatedly apply learnt filters to input images to produce feature maps that list the features present in the image.…”

Section: Deep Learning Methodologiesmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

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Section: Deep Learning Methodologiesmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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“… Max-Pooling (max_pooling2d): The pooling layer is useful in reducing the number of dimensions of the data. Pooling not only reduces the consumption of computing resources but also improves overall performance [ 53 ]. Max-pooling helps optimize the feature space by identifying the maximum value of elements from every pool, thereby achieving scale invariance [ 54 ].…”

Section: Sqi—modelling and Designmentioning

confidence: 99%

Artificial Intelligence-Based Smart Quality Inspection for Manufacturing

Sundaram

Zeid

2023

Micromachines

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In today’s era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of the Things (IoT) have made real-time tracking of systems a reality. The health of a product can also be continuously assessed throughout the manufacturing lifecycle by using Quality Control (QC) measures. Quality inspection is one of the critical processes in which the product is evaluated and deemed acceptable or rejected. The visual inspection or final inspection process involves a human operator sensorily examining the product to ascertain its status. However, there are several factors that impact the visual inspection process resulting in an overall inspection accuracy of around 80% in the industry. With the goal of 100% inspection in advanced manufacturing systems, manual visual inspection is both time-consuming and costly. Computer Vision (CV) based algorithms have helped in automating parts of the visual inspection process, but there are still unaddressed challenges. This paper presents an Artificial Intelligence (AI) based approach to the visual inspection process by using Deep Learning (DL). The approach includes a custom Convolutional Neural Network (CNN) for inspection and a computer application that can be deployed on the shop floor to make the inspection process user-friendly. The inspection accuracy for the proposed model is 99.86% on image data of casting products.

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“…Max pooling can exhibit the max value of the feature map in the

neighborhood. Not only the scale of the feature map space is optimized, but also the translation invariance of the network is preserved by max pooling [ 79 ].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…Figure 6 shows the process of average pooling. In average pooling, if the averages are low, the contrast will be diminished [ 79 ]. The convolutional features will be decreased [ 81 ] if most of the averages are zero.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

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A Comparison of Pooling Methods for Convolutional Neural Networks

Cited by 120 publications

References 72 publications

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Artificial Intelligence-Based Smart Quality Inspection for Manufacturing

A Survey on Deep Learning in COVID-19 Diagnosis

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