Cargando…

Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

SIMPLE SUMMARY: To enhance the automatic diagnosis of the prostate cancer using machine learning algorithm, we modify the design of convolutional neural network to support multi-scale denoising of cancer images. Transfer learning is employed to leverage the detection accuracy of the prostate cancer...

Descripción completa

Detalles Bibliográficos
Autores principales: Chui, Kwok Tai, Gupta, Brij B., Chi, Hao Ran, Arya, Varsha, Alhalabi, Wadee, Ruiz, Miguel Torres, Shen, Chien-Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9367349/
https://www.ncbi.nlm.nih.gov/pubmed/35954350
http://dx.doi.org/10.3390/cancers14153687
_version_ 1784765777224663040
author Chui, Kwok Tai
Gupta, Brij B.
Chi, Hao Ran
Arya, Varsha
Alhalabi, Wadee
Ruiz, Miguel Torres
Shen, Chien-Wen
author_facet Chui, Kwok Tai
Gupta, Brij B.
Chi, Hao Ran
Arya, Varsha
Alhalabi, Wadee
Ruiz, Miguel Torres
Shen, Chien-Wen
author_sort Chui, Kwok Tai
collection PubMed
description SIMPLE SUMMARY: To enhance the automatic diagnosis of the prostate cancer using machine learning algorithm, we modify the design of convolutional neural network to support multi-scale denoising of cancer images. Transfer learning is employed to leverage the detection accuracy of the prostate cancer detection model by taking advantages from more unseen data from a source dataset. Compared to existing methodologies, our work improves the accuracy by more than 10%. Ablation studies have conducted to evaluate the contributions of the components of the proposed algorithm, with 2.80%, 3.30%, and 3.13% for image denoising, multi-scale scheme, and transfer learning, respectively. The results reveal the effectiveness of the algorithm and provide insights for five future research directions. ABSTRACT: Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection. Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset. Results: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively. Conclusions: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.
format Online
Article
Text
id pubmed-9367349
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-93673492022-08-12 Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection Chui, Kwok Tai Gupta, Brij B. Chi, Hao Ran Arya, Varsha Alhalabi, Wadee Ruiz, Miguel Torres Shen, Chien-Wen Cancers (Basel) Article SIMPLE SUMMARY: To enhance the automatic diagnosis of the prostate cancer using machine learning algorithm, we modify the design of convolutional neural network to support multi-scale denoising of cancer images. Transfer learning is employed to leverage the detection accuracy of the prostate cancer detection model by taking advantages from more unseen data from a source dataset. Compared to existing methodologies, our work improves the accuracy by more than 10%. Ablation studies have conducted to evaluate the contributions of the components of the proposed algorithm, with 2.80%, 3.30%, and 3.13% for image denoising, multi-scale scheme, and transfer learning, respectively. The results reveal the effectiveness of the algorithm and provide insights for five future research directions. ABSTRACT: Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection. Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset. Results: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively. Conclusions: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain. MDPI 2022-07-28 /pmc/articles/PMC9367349/ /pubmed/35954350 http://dx.doi.org/10.3390/cancers14153687 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chui, Kwok Tai
Gupta, Brij B.
Chi, Hao Ran
Arya, Varsha
Alhalabi, Wadee
Ruiz, Miguel Torres
Shen, Chien-Wen
Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection
title Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection
title_full Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection
title_fullStr Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection
title_full_unstemmed Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection
title_short Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection
title_sort transfer learning-based multi-scale denoising convolutional neural network for prostate cancer detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9367349/
https://www.ncbi.nlm.nih.gov/pubmed/35954350
http://dx.doi.org/10.3390/cancers14153687
work_keys_str_mv AT chuikwoktai transferlearningbasedmultiscaledenoisingconvolutionalneuralnetworkforprostatecancerdetection
AT guptabrijb transferlearningbasedmultiscaledenoisingconvolutionalneuralnetworkforprostatecancerdetection
AT chihaoran transferlearningbasedmultiscaledenoisingconvolutionalneuralnetworkforprostatecancerdetection
AT aryavarsha transferlearningbasedmultiscaledenoisingconvolutionalneuralnetworkforprostatecancerdetection
AT alhalabiwadee transferlearningbasedmultiscaledenoisingconvolutionalneuralnetworkforprostatecancerdetection
AT ruizmigueltorres transferlearningbasedmultiscaledenoisingconvolutionalneuralnetworkforprostatecancerdetection
AT shenchienwen transferlearningbasedmultiscaledenoisingconvolutionalneuralnetworkforprostatecancerdetection