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Design of a Hybrid Intelligent System for Transitional Bladder Cell Carcinoma Diagnosis
2009
˜Al-œRafidain journal for computer sciences and mathematics
Preserved Fulltext
cancers of the urinary tract, accounting for approximately 90% of Bladder cancers. ...
In this research a new computer-based system "Design of a Hybrid Intelligent System for Transitional Bladder Cell Carcinoma Diagnosis" (DHSTCCD) has been proposed and implemented. ...
Conclusions In this research we have proposed a system for computerized diagnosis of Transitional bladder cell carcinoma, based on fuzzy-neural networks and fuzzy expert system. ...
doi:10.33899/csmj.2009.163763
fatcat:6idjlwx6kvbpllmp4y6nt7hpny
Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature
2021
Journal of Clinical Medicine
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been widely adopted to deal with numerous disorders, irrespective of their severity, extending from conditions such as benign prostate hyperplasia to critical illnesses such as urothelial and prostate cancer ...
A neural network that has a significant number of layers is called a deep learning network. ...
An artificial neural network is the basis of deep learning and a subfield of machine learning. ...
doi:10.3390/jcm10091864
pmid:33925767
fatcat:o2yugbxsmfbmbjidwtgzdno5am
A survey on computational intelligence approaches for predictive modeling in prostate cancer
2017
Expert systems with applications
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Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. ...
Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimis- * ing the performance of prostate ...
Neural networks are also effective for early diagnosis of prostate cancer when integrated into expert systems. ...
doi:10.1016/j.eswa.2016.11.006
fatcat:ii6gbq6qcbai5kxvcy4l7kkg54
Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images
[article]
2017
bioRxiv
pre-print
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Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human ...
We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. ...
Before deep neural networks (DNN) gained popularity, they were considered hard to train large networks efficiently for a long time. ...
doi:10.1101/197517
fatcat:umdydyffdfbpnodftitnagqqoq
Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images
2018
EBioMedicine
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Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human ...
We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. ...
Before deep neural networks (DNN) gained popularity, they were considered hard to train large networks efficiently for a long time. ...
doi:10.1016/j.ebiom.2017.12.026
pmid:29292031
pmcid:PMC5828543
fatcat:vtivfbpsv5gmnd5uma7lxw4any
Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review
2021
Diagnostic Pathology
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AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. ...
AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating ...
They integrated urinary bladder cancer arrays with artificial neural networks for this purpose. ...
doi:10.1186/s13000-021-01085-4
pmid:33731170
pmcid:PMC7971952
fatcat:wxc5ynqktrazbflkprscevuabu
Radyasyon Onkolojisinde Makine Öğrenmesi
2020
OSMANGAZİ JOURNAL OF MEDICINE
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Deep learning (DL) is an ML technique that utilizes deep neural networks to construct a model. ...
The RT stage is divided into seven groups as patient assessment, simulation, contouring, planning, quality assessment (QA), treatment application, and patient followup. ...
Deep learning (DL) is an ML technique that utilizes deep neural networks to construct a model. ...
doi:10.20515/otd.691331
fatcat:p7igtjxxmfgg5ghriouenqndoa
Identification of 12 cancer types through genome deep learning
2019
Scientific Reports
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We have proposed a method, GDL (genome deep learning), to study the relationship between genomic variations and traits based on deep neural networks. ...
Cancer is a major cause of death worldwide, and an early diagnosis is required for a favorable prognosis. ...
However, deep learning models can take entire genome variations into account without the influence of segregate sites. Neural network algorithms are inspired by biological neural networks. ...
doi:10.1038/s41598-019-53989-3
pmid:31754222
pmcid:PMC6872744
fatcat:pxwnmtjxlfeczmrqdxibefic7a
The Role of Artificial Intelligence in Early Cancer Diagnosis
2022
Cancers
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We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis ...
Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. ...
Acknowledgments: The authors would like to thank Stan Kaye for his invaluable support and feedback on this manuscript.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/cancers14061524
pmid:35326674
pmcid:PMC8946688
fatcat:bzcgndsievgzhajxh2sumudlqe
Cancer Diagnosis Using Deep Learning: A Bibliographic Review
2019
Cancers
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In particular, deep neural networks can be successfully used for intelligent image analysis. ...
The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art ...
Sabbaghi et al. used deep neural networks and mapped the images to enhance classification accuracy into bag-of-feature (BoF) space [167] . ...
doi:10.3390/cancers11091235
pmid:31450799
pmcid:PMC6770116
fatcat:ktuuttdu6zc7phj3mahp5yynxq
Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology
2020
Frontiers in Pharmacology
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Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. ...
Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. ...
Deep Neural Networks: For Multi-Omics Data Integration Deep neural networks (DNNs) are a subset of machine learning, which is gaining popularity in precision medicine. ...
doi:10.3389/fphar.2020.01177
pmid:32903628
pmcid:PMC7438594
fatcat:u7mdynhnwfazbn6jhvcagorp2a
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
[article]
2022
arXiv
pre-print
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Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. ...
The extraction of minable information from images gives way to the field of radiomics and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. ...
In the other words, these rules make the (deep) neural network biased toward the physicians' decision. ...
arXiv:2110.10332v4
fatcat:vmpxhoolarbrve5ddyfn5umfim
Artificial intelligence technology in oncology: a new technological paradigm
[article]
2019
arXiv
pre-print
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Results also suggest that this new technology can generate a technological paradigm shift for diagnostic assessment of any cancer type. ...
Moreover, this study shows the comparative evolutionary pathways of this emerging technology for three critical cancers: lung, breast and thyroid. ...
Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech ...
arXiv:1905.06871v1
fatcat:wtzcracchneghlon4t4vuqqkhu
Development of a radiomic signature for predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer
2021
Canadian Urological Association Journal
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Neoadjuvant chemotherapy (NAC) for muscle-invasive bladder cancer (MIBC) improves overall survival, but pathological response rates are low. ...
Methods: An institutional bladder cancer database was used to identify MIBC patients who were treated with NAC followed by radical cystectomy. ...
treatment response that used a combination of both deep-learning neural networks and radiomic technology among 123 patients with response to NAC assessed by post-treatment CT imaging. ...
doi:10.5489/cuaj.7294
pmid:34672933
pmcid:PMC8923886
fatcat:cb5mws5vcfhjff5dhzmgxmoibq
Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)
2021
Diagnostics
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There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. ...
Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. ...
FocalNet is integrated into an existing task-driven deep learning model without modifying the weights of the network, and layers for performing foveation are automatically selected using a data-driven ...
doi:10.3390/diagnostics11050742
pmid:33919342
pmcid:PMC8143297
fatcat:d5k6655lbfamzhjleyso5q54u4
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