Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images

Kwak, Min Seob; Lee, Hun Hee; Yang, Jae Min; Myung, Jae; Jeon, Jung Won; Yoon, Jin Young; Kim, Ha Il

doi:10.3389/fonc.2020.619803

Cited by 36 publications

(25 citation statements)

References 47 publications

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“…Artificial neural network (ANN) is a mathematical or computational model that uses structures similar to synaptic connections in the brain to process information [ 9 ]. ANN models have been applied to risk assessment of many diseases, including colon cancer, lung cancer, hepatocellular carcinoma, meningioma, and so on and have shown reliable and accurate performance in disease prediction and evaluation [ 10 – 13 ]. However, no studies have been reported on predicting prostate cancer risk based on ANN models.…”

Section: Introductionmentioning

confidence: 99%

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

Dong

Wang

2022

Journal of Healthcare Engineering

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This study aimed to establish an artificial neural network (ANN) model based on prostate cancer signature genes (PCaSGs) to predict the patients with prostate cancer (PCa). In the present study, 270 differentially expressed genes (DEGs) were identified between PCa and normal prostate (NP) groups by differential gene expression analysis. Next, we performed Metascape gene annotation, pathway and process enrichment analysis, and PPI enrichment analysis on all 270 DEGs. Then, we identified and screened out 30 PCaSGs based on the random forest analysis and constructed an ANN model based on the gene score matrix consisting of 30 PCaSGs. Lastly, analysis of microarray dataset GSE46602 showed that the accuracy of this model for predicating PCa and NP samples was 88.9 and 78.6%, respectively. Our results suggested that the ANN model based on PCaSGs can be used for effectively predicting the patients with PCa and will be helpful for early PCa diagnosis and treatment.

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Section: Introductionmentioning

confidence: 99%

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

Dong

Wang

2022

Journal of Healthcare Engineering

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“…The images are representative of stages 1 through 3 of cancer, which means that the cancer has not yet spread to other organs. [32] The polyps are the growths that can be become the tumors, some studies directly analyze them, since there is a chance, it does not become cancerous. Patel et al in their study trained a number of six CNN on their dataset.…”

Section: B Ai Diagnosis For Colorectal Cancermentioning

confidence: 99%

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

et al. 2022

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Cancer is one of the deadliest diseases in the present days. Its survivability is mostly corelated to early detection and treatment, which means that it is of utmost importance to successfully diagnose the patients. Unfortunately, even with years of experience human errors can happen which leads to the death of many individuals being misdiagnosed. Throughout the years there have been several applications created which could possibly aid doctors in the diagnosis. Neural Networks have always been a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human's computational capabilities. In image processing for example, a convolutional neural network can analyze each particular pixel and determine through the convolution function the common properties of different pictures. The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods. The analyzed factors are training parameters, image processing technique and the obtained performances. This survey/review can be of significant value to researchers and professionals in medicine and computer science, highlighting areas where there are opportunities to make significant new contributions. INDEX TERMSCancer diagnosis, Image processing, Neural networks I.

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“…Machine learning approaches started to improve medical care and biomedical research in recent years. It has been used in fields such as radiology, telehealth, clinical care, and even stem cell biology [20,[31][32][33].…”

Section: Microscopic Preservation Of Cardiac Ecmmentioning

confidence: 99%

Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks

et al. 2022

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Whole organ decellularization techniques have facilitated the fabrication of extracellular matrices (ECMs) for engineering new organs. Unfortunately, there is no objective gold standard evaluation of the scaffold without applying a destructive method such as histological analysis or DNA removal quantification of the dry tissue. Our proposal is a software application using deep convolutional neural networks (DCNN) to distinguish between different stages of decellularization, determining the exact moment of completion. Hearts from male Sprague Dawley rats (n = 10) were decellularized using 1% sodium dodecyl sulfate (SDS) in a modified Langendorff device in the presence of an alternating rectangular electric field. Spectrophotometric measurements of deoxyribonucleic acid (DNA) and total proteins concentration from the decellularization solution were taken every 30 min. A monitoring system supervised the sessions, collecting a large number of photos saved in corresponding folders. This system aimed to prove a strong correlation between the data gathered by spectrophotometry and the state of the heart that could be visualized with an OpenCV-based spectrometer. A decellularization completion metric was built using a DCNN based classifier model trained using an image set comprising thousands of photos. Optimizing the decellularization process using a machine learning approach launches exponential progress in tissue bioengineering research.

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Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images

Cited by 36 publications

References 47 publications

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks

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