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Design of a Hybrid Intelligent System for Transitional Bladder Cell Carcinoma Diagnosis

Nada Saleem, Khalid Saleem
2009 ˜Al-œRafidain journal for computer sciences and mathematics  
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

B M Zeeshan Hameed, Aiswarya V L S Dhavileswarapu, Syed Zahid Raza, Hadis Karimi, Harneet Singh Khanuja, Dasharathraj K Shetty, Sufyan Ibrahim, Milap J Shah, Nithesh Naik, Rahul Paul, Bhavan Prasad Rai, Bhaskar K Somani
2021 Journal of Clinical Medicine  
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

Georgina Cosma, David Brown, Matthew Archer, Masood Khan, A. Graham Pockley
2017 Expert systems with applications  
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]

Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha
2017 bioRxiv   pre-print
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

Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha
2018 EBioMedicine  
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

Zubair Ahmad, Shabina Rahim, Maha Zubair, Jamshid Abdul-Ghafar
2021 Diagnostic Pathology  
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

Melek AKÇAY, Durmuş ETİZ
2020 OSMANGAZİ JOURNAL OF MEDICINE  
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

Yingshuai Sun, Sitao Zhu, Kailong Ma, Weiqing Liu, Yao Yue, Gang Hu, Huifang Lu, Wenbin Chen
2019 Scientific Reports  
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

Benjamin Hunter, Sumeet Hindocha, Richard W. Lee
2022 Cancers  
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

Khushboo Munir, Hassan Elahi, Afsheen Ayub, Fabrizio Frezza, Antonello Rizzi
2019 Cancers  
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

Sandip Kumar Patel, Bhawana George, Vineeta Rai
2020 Frontiers in Pharmacology  
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]

Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim
2022 arXiv   pre-print
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]

Mario Coccia
2019 arXiv   pre-print
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

Ambica Parmar, Abdul Aziz Qazi, Audrius Stundzia, Hao-Wen Sim, Jeremy Lewin, Ur Metser, Martin O'Malley, Aaron R. Hansen
2021 Canadian Urological Association Journal  
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)

Rima Hajjo, Dima A Sabbah, Sanaa K Bardaweel, Alexander Tropsha
2021 Diagnostics  
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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