Wavelet Pooling Scheme in the Convolution Neural Network (CNN) for Breast Cancer Detection

Onjun, Ratapong; Sriwichai, Kittikorn; Dungkratoke, Narongdech; Kaennakham, Sayan

doi:10.3233/faia220426

Cited by 3 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different wavelet bases have a substantial influence on the resulting time-frequency map. In the context of CNNs, WT is developed and employed for breast cancer detection [42]. The adaptive wavelet pooling layers, as proposed by the Wolter et al [43], make use of fast WT (FWT) to lower the feature resolution.…”

Section: Wavelets and Cnnmentioning

confidence: 99%

Approximating Partial Differential Equations with Physics-Informed Legendre Multiwavelets CNN

Wang,

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

The purpose of this paper is to leverage the advantages of physics-informed neural network (PINN) and convolutional neural network (CNN) by using Legendre multiwavelets (LMWs) as basis functions to approximate partial differential equations (PDEs). We call this method Physics-Informed Legendre Multiwavelets CNN (PiLMWs-CNN), which can continuously approximate a grid-based state representation that can be handled by a CNN. PiLMWs-CNN enable us to train our models using only physics-informed loss functions without any precomputed training data, simultaneously providing fast and continuous solutions that generalize to previously unknown domains. In particular, the LMWs can simultaneously possess compact support, orthogonality, symmetry, high smoothness, and high approximation order. Compared to orthonormal polynomial (OP) bases, the approximation accuracy can be greatly increased and computation costs can be significantly reduced by using LMWs. We applied PiLMWs-CNN to approximate the damped wave equation, the incompressible Navier–Stokes (N-S) equation, and the two-dimensional heat conduction equation. The experimental results show that this method provides more accurate, efficient, and fast convergence with better stability when approximating the solution of PDEs.

show abstract

Section: Wavelets and Cnnmentioning

confidence: 99%

Approximating Partial Differential Equations with Physics-Informed Legendre Multiwavelets CNN

Wang,

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

show abstract

“…Nawaz et al [5] achieved 95.4% accuracy using DenseNet CNNs on histopathological images. Onjun et al [6] enhanced CNNs with wavelet transformation, reaching 96.49% accuracy. Sriwichai et al [7] proposed WT-CNN hybrids for improved breast cancer detection.…”

Section: Introductionmentioning

confidence: 99%

On the Wavelet Convolution Neural Networks for Ultra-Sound Based Breast Cancer Detection

Sriwichai,

Simtrakankul,

Onjun

et al. 2023

Fuzzy Systems and Data Mining IX

View full text Add to dashboard Cite

Breast cancer is one of the deadliest types of cancer, and it comes in a wide variety of forms, resulting in a wide variety of detection methods. Several deep learning techniques have been applied to decrease unnecessary biopsies and lessen the burden on radiologists. One of the most popular architectures for this task is the Convolutional Neural Networks (CNNs). This paper aims to explore the integration of convolutional neural networks (CNN) and wavelet transform (WT) to identify the optimal combination and architecture of these methods for efficient detection of breast cancer in ultrasound images. To accomplish this task, the wavelet convolutional neural network (WCNN) structures are proposed and trained for the mission of screening breast cancer abnormalities embedded in ultrasound type of images. Compared with other two popular networks, ResNet50 and MobileNetV2, it has been found that the proposed WCNN has produced a satisfactory solution, with an accuracy of 98.24%, precision of 97.29%, recall of 100%, and F measure of 98.24%.

show abstract

Wavelet Pooling in Convolutional Neural Networks for Breast Cancer Detection with Ultrasound Images

Onjun,

Dungkratoke,

Sriwichai

et al. 2023

Mechanisms and Machine Science

View full text Add to dashboard Cite

Wavelet Pooling Scheme in the Convolution Neural Network (CNN) for Breast Cancer Detection

Cited by 3 publications

References 13 publications

Approximating Partial Differential Equations with Physics-Informed Legendre Multiwavelets CNN

Approximating Partial Differential Equations with Physics-Informed Legendre Multiwavelets CNN

On the Wavelet Convolution Neural Networks for Ultra-Sound Based Breast Cancer Detection

Wavelet Pooling in Convolutional Neural Networks for Breast Cancer Detection with Ultrasound Images

Contact Info

Product

Resources

About