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Cellular neural networks and computational intelligence in medical image processing

I. Aizenberg, N. Aizenberg, J. Hiltner, C. Moraga, E. Meyer zu Bexten
2001 Image and Vision Computing  
The paper focuses mainly on neural networks in medical image processing.  ...  The principal constituents of computational intelligence are fuzzy logic, neural networks and evolutionary algorithms, with emphasis in their mutual enhancement.  ...  Overview of computational intelligence in medical image processing Neural networks are well known for their good performance in classification and function approximation.  ... 
doi:10.1016/s0262-8856(00)00066-4 fatcat:tr4d3v7tgvgq7lcjhbi7ctydwy


Matthew N. O. Sadiku, Mahamadou Tembely, Sarhan M. Musa
2018 International Journal of Advanced Research in Computer Science and Software Engineering  
Natural computing refers to the field that investigates both human-designed computing inspired by nature and computing taking place in nature. It is based on the premise that "nature computes."  ...  Due of its interdisciplinary nature, natural computing covers a spectrum of research areas including biology, chemistry, physics, computer science, and engineering.  ...  The "classical" nature-inspired models of computation are cellular automata, neural computation, and evolutionary computation.  ... 
doi:10.23956/ijarcsse.v8i5.597 fatcat:hmuhtsk4rjbgdfbzjjcndir7zu

CNN Technology for Spatiotemporal Signal Processing

David López Vilariño, Diego Cabello Ferrer, Víctor Manuel Brea Sánchez, Ronald Tetzlaff, Chin-Teng Lin
2009 EURASIP Journal on Advances in Signal Processing  
Cellular Neural Networks (CNNs) are a paradigm for nonlinear spatial-temporal dynamics and the core of the Cellular Wave Computing (also called CNN technology).  ...  Let us name, as example, image processing techniques based on active wave propagation, or applications within the medical image processing framework, where fast processing provides new capabilities for  ...  Cellular Neural Networks (CNNs) are a paradigm for nonlinear spatial-temporal dynamics and the core of the Cellular Wave Computing (also called CNN technology).  ... 
doi:10.1155/2009/854806 fatcat:nv5jb4vrtbd2bagz2fezmtp3tq

An intelligent mobile based decision support system for retinal disease diagnosis

A. Bourouis, M. Feham, M.A. Hossain, L. Zhang
2014 Decision Support Systems  
This mobile diagnosis system uses an artificial Neural Network algorithm to analyze the retinal images captured by the microscopic lens to identify retinal disease conditions.  ...  The application is optimized by using the rooted method in order to increase battery lifetime and processing capacity.  ...  The Neural Network is initially trained with healthy and infected retinal images on a personal computer and then embedded in an Android environment.  ... 
doi:10.1016/j.dss.2014.01.005 fatcat:3n5knnrqmfadvgfzu62ggvvu5e

M-Health: Skin Disease Analysis System Using Smartphone's Camera

Abderrahim Bourouis, Ali Zerdazi, Mohammed Feham, Abdelhamid Bouchachia
2013 Procedia Computer Science  
In this paper, an Artificial Neural Network algorithm is used to analyze the skin images to identify cancer.  ...  The increasing use of Smartphone and Tablet , their on-board sensors and increasing computational power show immense promise for improved mobile health technologies , one of the medical areas which needs  ...  However, The neural network algorithm is trained and tested in computer using skin datasets of normal and abnormal state , see Fig.1 .  ... 
doi:10.1016/j.procs.2013.06.157 fatcat:2mhkxthsnvcwhe554a5yjidwpy

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology

Ramanaesh Rao Ramakrishna, Zariyantey Abd Hamid, Wan Mimi Diyana Wan Zaki, Aqilah Baseri Huddin, Ramya Mathialagan
2020 PeerJ  
Deep learning rectifies data features using a convolutional neural network (CNN), a type of multi-layered neural network that can play an innovative role in image recognition.  ...  This review discusses the progress and future of CNNs in stem cell imaging for therapy and research.  ...  CNNs are one of the architectural types for deep neural networks and come from a family of multi-layer neural networks principally constructed for two-dimensional data processing, such as images and videos  ... 
doi:10.7717/peerj.10346 pmid:33240655 pmcid:PMC7680049 fatcat:ej7vg2gukbdcrahelaw2cpofc4

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Muhammad Javed Iqbal, Zeeshan Javed, Haleema Sadia, Ijaz A. Qureshi, Asma Irshad, Rais Ahmed, Kausar Malik, Shahid Raza, Asif Abbas, Raffaele Pezzani, Javad Sharifi-Rad
2021 Cancer Cell International  
The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow  ...  Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual.  ...  DL can process data including medical images by Artificial neural network (ANN) to mimic the human neural architecture and is composed of input, output, and various hidden multi-layer networks to enhance  ... 
doi:10.1186/s12935-021-01981-1 pmid:34020642 fatcat:2q6aeg4jobdlplcjhemadavez4

Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Fuqian Shi, Gaoxiang Chen, Yu Wang, Ningning Yang, Yating Chen, Nilanjan Dey, R. Simon Sherratt
2019 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)  
Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance.  ...  Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment.  ...  Hence, artificial neural networks have a significant role in medical image analysis and classification. For microscopic liver image analysis and classification, Akram et al.  ... 
doi:10.1109/itaic.2019.8785563 fatcat:tgrb7zxctfggdoiutcu6si3r2e

Artificial Intelligence and Data Mining 2014

Fuding Xie, Suohai Fan, Jianzhou Wang, Helen Lu, Caihong Li
2014 Abstract and Applied Analysis  
and novel data mining techniques; (iii) computational intelligence in medical science and biology; (iv) time series analysis in economics and finance; (v) machine learning on massive datasets.  ...  In "Recognition of process disturbances for an SPC/EPC stochastic system using support vector machine and artificial neural network approaches," authored by Y. E.  ...  Acknowledgments The guest editors of this special issue would like to express their thanks to the authors who have submitted papers for consideration and the referees of the submitted papers.  ... 
doi:10.1155/2014/819641 fatcat:ktzo3xoqn5f4zkg6jienbohscq

Artificial intelligence in musculoskeletal oncological radiology

Matjaz Vogrin, Teodor Trojner, Robi Kelc
2020 Radiology and Oncology  
In order to make the diagnosis more precise and to prevent the overlooking of potentially dangerous conditions, artificial intelligence has been continuously incorporated into medical practice in recent  ...  a more efficient diagnosis of bone and soft tissue tumors.  ...  These present the input cells (in pink) a neural network. For each ROI the neural network extracts and compute features within the hidden layers (in grey) by using pre-trained data sets.  ... 
doi:10.2478/raon-2020-0068 pmid:33170144 pmcid:PMC7877260 fatcat:mmuyqrqwjjgqpenubkdpadxz7e

Opportunities for artificial intelligence in advancing precision medicine [article]

Fabian V. Filipp
2019 arXiv   pre-print
State-of-the-art applications of deep neural networks include digital image recognition, single cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine.  ...  Machine learning (ML), deep learning (DL), and artificial intelligence (AI) are of increasing importance in biomedicine.  ...  Machine intelligence and deep networks are changing our approach to medical bioinformatics at an unprecedented speed.  ... 
arXiv:1911.07125v1 fatcat:6qtnyy5prvewlpqp3wuiv45cji

IEEE Access Special Section Editorial: Scalable Deep Learning for Big Data

Liangxiu Han, Daoqiang Zhang, Omer Rana, Yi Pan, Sohail Jabbar, Mazin Yousif, Moayad Aloqaily
2020 IEEE Access  
, IEEE TRANSACTIONS ON IMAGE PROCESSING, NeuroImage, Human Brain Mapping, and Medical Image Analysis and in conference proceedings, such as IJCAI, AAAI, NIPS, CVPR, MICCAI, KDD, with over 12 000 citations  ...  In these areas, he has published over 200 scientific articles in refereed international journals, such as IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE TRANSACTIONS ON MEDICAL IMAGING  ... 
doi:10.1109/access.2020.3041166 fatcat:zkzdnzk22jge3l5mwju3j42mcu

An artificial intelligent diagnostic system on differential recognition of hematopoietic cells from microscopic images

Meral Beksa�, M. Sinan Beksa�, V. Bahad?r Tipi, H. Ali Duru, M. �mit Karaka?, A. Nur �akar
1997 Cytometry  
Image processing and analysis were used to obtain 13 cellular features to be used as the input parameters (neurons) of the artificial neural network.  ...  A supervised artificial neural network (back-propagation learning algorithm) was used in the classification of 16 different cells (output neurons of the neural network), which is the second step of pattern  ...  Hamdi Akan, and Dr. Osman Ilhan for giving us their patients' slides. LITERATURE CITED  ... 
doi:10.1002/(sici)1097-0320(19970615)30:3<145::aid-cyto5>;2-k pmid:9222100 fatcat:fmtmcvegsffzfmaspscy2m4a2q

Survey on Neural Networks Used for Medical Image Processing

Zhenghao Shi, Lifeng He, Kenji Suzuki, Tsuyoshi Nakamura, Hidenori Itoh
2009 International Journal of Computational Science  
This paper aims to present a review of neural networks used in medical image processing. We classify neural networks by its processing goals and the nature of medical images.  ...  By this survey, we try to answer the following two important questions: (1) What are the major applications of neural networks in medical image processing now and in the nearby future?  ...  Acknowledgements The authors are grateful to the anonymous referees for their constructive and helpful comments.  ... 
pmid:26740861 pmcid:PMC4699299 fatcat:4scj7gtidva73k3xzls346punq

Abnormality Detection from X-Ray Bone Images using DenseNet Convolutional Neural Network

Shukla Abhilash, Patel Atul
2021 International Journal of Current Research and Review  
This paper shows how Artificial Intelligence especially the Convolutional Neural Network of Deep Learning can be used to detect abnormality from X-Ray bone images.  ...  Table and Confusion Matrix methods are used to evaluate the performance of DenseNet (Densely Connected Convolutional Networks) Model for the detection of abnormality in bone from X-Ray Images.  ...  ACKNOWLEDGEMENT The authors acknowledge the support received from the researcher whose articles are cited and included in references to this manuscript.  ... 
doi:10.31782/ijcrr.2021.131026 fatcat:3tufrrm3xzffne52mrq3agb2nu
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