/GS8 27 0 R /FontName /Times#20New#20Roman This study investigated the use of ANNs for diagnostic and prognostic purposes in pancreatic disease, especially acute … /Kids [4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R 13 0 R 14 0 R] << 77: 145-153, 1994. >> Comput Meth Progr Biomed. [1] “Viral Hepatitis,” 2020. https://my.clevelandclinic.org/health/diseas es/4245-hepatitis-viral-hepatitis-a-b--c (accessed May 17, … 106: 55-66, 2012. /StructParents 10 /Length1 55544 /GS9 26 0 R /CS /DeviceRGB /ExtGState >> /AvgWidth 401 /Type /Page >> >> << 59: 190-194, 2012. /F5 21 0 R >> J Microbiol Meth. 17 0 obj /GS9 26 0 R The real procedure of medical diagnosis which usually is employed by physicians was analyzed and converted to a machine implementable format. 12 0 obj << << 33: 335-339, 2012. Wiley VCH, Weinheim, 380 p. 1999. >> /Font Arnold M. Non-invasive glucose monitoring. /Group An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. >> Cytometry B Clyn Cytom. 59: 190-194, 2012. /F7 31 0 R >> HEART DISEASES DIAGNOSIS USING ARTIFICIAL NEURAL NETWORKS Freedom of Information: Freedom of Information Act 2000 (FOIA) ensures access to any information held by Coventry University, including theses, unless an exception or exceptional circumstances apply. /S /Transparency /Type /Group /CS /DeviceRGB In such activity, the application of artificial neural networks is become very popular in fault diagnosis, where the damage indicators and signal features are classified in an automatic way. /Annotation /Sect /S /Transparency Neuroradiology. /Contents 34 0 R /Diagram /Figure 7: e29179, 2012. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. /Count 11 Artificial Neural Network (ANN)-based diagnosis of medical diseases has been taken into great consideration in recent years. /XHeight 250 /Resources The role of computer technologies is now increasing in the diagnostic procedures. 35: 329-332, 2011. /Worksheet /Part In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. /Type /Page /StructParents 2 /Type /FontDescriptor >> 4: 29, 2005. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /Parent 2 0 R /MediaBox [0 0 595.2 841.92] J Med Syst. /GS8 27 0 R /ExtGState Artificial neural network is a technique which tries to simulate behavior of the neurons in humans’ brain. >> /GS9 26 0 R Tuberculosis is important health problem in Turkey also. Artificial neural networks with their own data try to determine if a 93: 72-78, 2012. /Resources /StructParents 3 WASET. /Contents 42 0 R /ExtGState << /S /Transparency >> /Type /Page Amato et al. << Mazurowski M, Habas P, Zurada J, Lo J, Baker J, Tourassi G. Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. 349: 1851-1870, 2012. Due to the substantial plasticity of input data, ANNs have proven useful in the analysis of blood Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. Fedor P, Malenovsky I, Vanhara J, Sierka W, Havel J. Thrips (Thysanoptera) identification using artificial neural networks. The results of the experiments and also the advantages of using a fuzzy approach were discussed as well. >> Catalogna M, Cohen E, Fishman S, Halpern Z, Nevo U, Ben-Jacob E. Artificial neural networks based controller for glucose monitoring during clamp test. The goal of this paper is to evaluate artificial neural network in disease diagnosis. /AvgWidth 422 /F6 20 0 R << >> /Resources /F8 30 0 R << >> /Textbox /Sect /FontWeight 700 9 0 obj /Type /Group Narasingarao M, Manda R, Sridhar G, Madhu K, Rao A. /Tabs /S Finding biomarkers is getting easier. Curr Opin Biotech. << << 10 0 obj /Font 95: 817-826, 2008. /GS8 27 0 R >> /K [15 0 R] Earlier diagnosis of hypertension saves enormous lives, failing which may lead to other sever problems causing sudden fatal end. /Type /StructTreeRoot /FontFile2 48 0 R endobj >> >> /Length 21590 47 0 obj /MediaBox [0 0 595.2 841.92] Mortazavi D, Kouzani A, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. 19: 411-434, 2006. /Widths 44 0 R Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural networks. << /F1 25 0 R /ExtGState For detecting crop disease early and accurately, a system is developed using image processing techniques and artificial neural network. /Type /Group 57: 4196-4199, 1997. 13 0 obj /F7 31 0 R endobj /F1 25 0 R One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. Anal Quant Cytol Histol. 34: 299-302, 2008. /Font Chest diseases are very serious health problems in the life of people. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /Font /Resources >> /GS8 27 0 R /Tabs /S 39: 323-334, 2000. 6 0 obj /F6 20 0 R The goal of this paper is to evaluate artificial neural network in disease diagnosis. >> >> RESEARCH ARTICLE Open Access Application of artificial neural network model in diagnosis of Alzheimer’s disease Naibo Wang1,2, Jinghua Chen1, Hui Xiao1, Lei Wu1*, Han Jiang3* and Yueping Zhou1 Abstract Background: Alzheimer’s disease has become a public health crisis globally due to its increasing incidence. endobj Trajanoski Z, Regittnig W, Wach P. Simulation studies on neural predictive control of glucose using the subcutaneous route. /Group two artificial neural networks created for the diagnosis of diseases in fish caused by protozoa and bacteria. Yan H, Zheng J, Jiang Y, Peng C, Xiao S. Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm. 21: 427-436, 2008. Dazzi D, Taddei F, Gavarini A, Uggeri E, Negro R, Pezzarossa A. /GS9 26 0 R PloS One. Havel J, Peña E, Rojas-Hernández A, Doucet J, Panaye A. Neural networks for optimization of high-performance capillary zone electrophoresis methods. << /Tabs /S << << /FirstChar 32 Appl Soft Comput. 24: 401-410, 2005. /F2 24 0 R /StructParents 6 /F6 20 0 R /MarkInfo >> /Type /Font J Parasitol. /Parent 2 0 R /Type /Group /Group For this purpose, two different MLNN structures were used. /CS /DeviceRGB 16: 231-236, 2010. /Tabs /S J Biomed Biotechnol. /Type /Page ;bSTg����نش�]��+V�%s���fz_��4]6y�3@E��6m`w:�t�vk�ˉ[(՞a˞�9����I�)M�M>��)͔̈́o��=�a�аisg��t�N�{�f�i��)/'$I�� N��pfg:\T:3r. 108: 80-87, 1988. Methods: We developed an approach for prediction of TB, based on artificial neural network … These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. /Type /Page /Type /Font Brougham D, Ivanova G, Gottschalk M, Collins D, Eustace A, O'Connor R, Havel J. /LastChar 122 Michalkova V, Valigurova A, Dindo M, Vanhara J. Larval morphology and anatomy of the parasitoid Exorista larvarum (Diptera: Tachinidae), with an emphasis on cephalopharyngeal skeleton and digestive tract. << >> /MediaBox [0 0 595.2 841.92] Artificial Neur Networks: Opening the Black Box. J Med Syst. Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes. Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance. /Name /F2 << /Footer /Sect /Group /Group /F7 31 0 R << In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). 7: 46-49, 1996. endobj /StructParents 1 /F9 29 0 R /GS8 27 0 R /F7 31 0 R << endobj Ann Intern Med. Ho W-H, Lee K-T, Chen H-Y, Ho T-W, Chiu H-C. Disease-free survival after hepatic resection in hepatocellular carcinoma patients: a prediction approach using artificial neural network. >> >> << /CS /DeviceRGB endobj >> >> J Appl Biomed 11:47-58, 2013 | DOI: 10.2478/v10136-012-0031-x. /Tabs /S >> 101: 165-175, 2010. << 54: 299-320, 2012a. /ExtGState /InlineShape /Sect Received: December 17, 2012; Published: July 31, 2013Show citation. Improving an Artificial Neural Network Model to Predict Thyroid Bending Protein Diagnosis Using Preprocessing Techniques. 33: 435-445, 2009. J Chromatogr A. Dayhoff J, Deleo J. J Cardiol. /CapHeight 693 Karabulut E, Ibrikçi T. Effective diagnosis of coronary artery disease using the rotation forest ensemble method. >> In the recent decades, Artificial Neural Networks (ANNs) are considered as the best solutions to achieve << /GS8 27 0 R Ahmed F. Artificial neural networks for diagnosis and survival prediction in colon cancer. /Parent 2 0 R << /FontBBox [-147 -263 1168 654] J Appl Biomed. /F6 20 0 R /XObject 54: 299-320, 2012b. /Group /Font 8 0 obj /Type /Group endobj /F8 30 0 R /GS9 26 0 R /F5 21 0 R /Contents 35 0 R /Type /Page /F6 20 0 R /F1 25 0 R >> /Descent -263 Dey P, Lamba A, Kumari S, Marwaha N. Application of an artificial neural network in the prognosis of chronic myeloid leukemia. << 14 0 obj Shankaracharya, Odedra D, Samanta S, Vidyarthi A. Computational intelligence in early diabetes diagnosis: a review. >> Eur J Gastroenterol Hepatol. 57: 127-133, 2009. /Type /Group 793: 317-329, 1998. Neuroradiology. << /Group 48 0 obj /MaxWidth 2614 38: 9799-9808, 2011. /Group >> Int Thomson Comput Press, London 1995. << Abstracts - Artificial Neural Networks (ANNs) play a vital role in the medical field in solving various health problems like acute diseases and even other mild diseases. Barwad A, Dey P, Susheilia S. Artificial neural network in diagnosis of metastatic carcinoma in effusion cytology. /Encoding /WinAnsiEncoding Breast cancer is a widespread type of cancer (for example in the UK, it’s the most common cancer). /Image34 33 0 R /Parent 2 0 R Pattern Recogn Lett. /Resources Uğuz H. A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases. This study demonstrated the ability of an artificial neural network to predict patient survival of hepatitis by analyzing hepatitis diagnostic results. /F3 23 0 R << /CS /DeviceRGB /Parent 2 0 R /Workbook /Document /CapHeight 654 Artificial neural networks for differential diagnosis of interstitial lung disease may be useful in clinical situations, and radiologists may be able to utilize the ANN output to their advantage in the differential diagnosis of interstitial lung disease on chest radiographs. >> /Resources The control of blood glucose in the critical diabetic patient: a neuro-fuzzy method. /Flags 32 The second is the heart disease; data is on cardiac Single Proton Emission Computed Tomography (SPECT) images. 25 0 obj >> In this study, a comparative hepatitis disease diagnosis study was realized. Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. Cancer Lett. /GS9 26 0 R Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks are finding many uses in the medical diagnosis application. 24 0 obj Tuberculosis Disease Diagnosis Using Artificial Neural Networks. /StructParents 7 >> Amato F, González-Hernández J, Havel J. 36: 168-174, 2011. /StructParents 0 79: 493-505, 2011. /ParentTreeNextKey 11 /F1 25 0 R Artificial Neural Network can be applied to diagnosing breast cancer. /Tabs /S /ExtGState Elveren E, Yumuşak N. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. Leon BS, Alanis AY, Sanchez E, Ornelas-Tellez F, Ruiz-Velazquez E. Inverse optimal neural control of blood glucose level for type 1 diabetes mellitus patients. Basheer I, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. /Contents 37 0 R 15: 80-87, 2001. de Bruijn M, ten Bosch L, Kuik D, Langendijk J, Leemans C, Verdonck-de Leeuw I. << 7: 252-262, 2010. /Type /Group << /StructParents 8 endobj /Marked true /Type /Page /MediaBox [0 0 595.2 841.92] J Assoc Physicians India. >> /Encoding /WinAnsiEncoding Er O, Temurtas F, Tanrikulu A. /F9 29 0 R /GS9 26 0 R /Tabs /S J Med Syst. Many methods have been developed for this purpose. /Resources %���� << 23: 1323-1335, 2002. Neur Networks. The training phase is the critical part of the process and need the availability of data of healthy and damaged cases. /Type /Group >> /GS9 26 0 R << The diagnosis of breast cancer is performed by a pathologist. 3 0 obj Özbay Y. Barbosa D, Roupar D, Ramos J, Tavares A and Lima C. Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images. << /StructParents 4 Chan K, Ling S, Dillon T, Nguyen H. Diagnosis of hypoglycemic episodes using a neural network based rule discovery system. /CS /DeviceRGB Standardizing clinical laboratory data for the development of transferable computer-based diagnostic programs. >> >> 36: 61-72, 2012. /F1 25 0 R << /CS /DeviceRGB endobj For this purpose, a probabilistic neural network structure was used. /S /Transparency This technique has had a wide usage in recent years. /MediaBox [0 0 595.2 841.92] Alkim E, Gürbüz E, Kiliç E. A fast and adaptive automated disease diagnosis method with an innovative neural network model. /StructParents 9 /Widths 46 0 R << Logoped Phoniatr Vocol. << 36: 3011-3018, 2012. /Lang (en-US) %PDF-1.5 /GS8 27 0 R /S /Transparency >> /Descent -216 Szolovits P, Patil RS, Schwartz W. Artificial Intelligence in Medical Diagnosis. J Med Syst. >> endobj /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] 209: 410-419, 2012. 95: 544-554, 2009. Thyroid disease diagnosis is an important capability of medical information systems. 91: 1615-1635, 2001. /F5 21 0 R Bull Entomol Res. Nowadays, one of the main issues to create challenges in medicine sciences by developing technology is the disease diagnosis with high accuracy. The System can be installed on the device. >> 2013;11(2):47-58. doi: 10.2478/v10136-012-0031-x. /MediaBox [0 0 595.2 841.92] >> /FontBBox [-568 -216 2046 693] 45: 257-265, 2012. << 2011: 158094, 2011. Sci Pharm. /S /Transparency /MediaBox [0 0 595.2 841.92] Talanta. << Artificial Neural Network (ANN) techniques to the diagnosis of diseases in patients. Background Alzheimer’s disease has become a public health crisis globally due to its increasing incidence. << /Type /Group Heart Diseases Diagnoses using Artificial Neural Network Noura Ajam Business Administration Collage- Babylon University Email: nhzijam@yahoo.com Abstract In this paper, attempt has been made to make use of Artificial Neural network in Disease Diagnosis with high accuracy. /GS9 26 0 R /F7 31 0 R Eur J Surg Oncol. Aleksander I, Morton H. An introduction to neural computing. Ecotoxicology. El-Deredy W, Ashmore S, Branston N, Darling J, Williams S, Thomas D. Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks Cancer Res. 98: 437-447, 2008. /ParentTree 16 0 R Through this experience, it appears that deep learning can provide significant help in the field of medicine and other fields. endobj /ExtGState << The system mainly includes various concepts related to image processing such as image acquisition, image pre-processing, feature extraction, creating database and classification by using artificial neural network. << /Ascent 891 However, the Artificial neural networks, Multilayer perceptron, Back- results of the experiments are somewhat confusing as they propagation algorithm, Coronary heart disease, Principal were presented in terms of ROC curves, Hierarchical Cluster Component Analysis Analysis (HCA) and Multidimensional Scaling (MDS) rather than the more popular percentage of accuracy approach. Neural networks. /F5 21 0 R Two cases are studied. /Parent 2 0 R Overview of Artificial neural network in medical diagnosis Seeking various uses in various fields of science, medical diagnosis field also has found the application of artificial neural network using biostatistics in clinical services. /Subtype /TrueType /F7 31 0 R /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /S /Transparency The aim of this study was to develop an artificial neural networks-based (ANNs) diagnostic model for coronary heart disease (CHD) using a complex of traditional and genetic factors of this disease. << The system for medical diagnosis using neural networks will help patients diagnose the disease without the need of a medical expert. /CS /DeviceRGB /Type /Group << >> J Cardiol. Comput Meth Progr Biomed. /Type /Page 50: 124-128, 2011. /F8 30 0 R /Type /Page /Flags 32 << /MediaBox [0 0 595.2 841.92] Atkov O, Gorokhova S, Sboev A, Generozov E, Muraseyeva E, Moroshkina S and Cherniy N. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. /Contents 32 0 R >> 33: 88-96, 2012. << Spelt L, Andersson B, Nilsson J, Andersson R. Prognostic models for outcome following liver resection for colorectal cancer metastases: A systematic review. /Tabs /S /StructTreeRoot 3 0 R /F8 30 0 R /F7 31 0 R /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /F5 21 0 R >> Kheirelseid E, Miller N, Chang K, Curran C, Hennessey E, Sheehan M, Newell J, Lemetre C, Balls G, Kerin M. miRNA expressions in rectal cancer as predictors of response to neoadjuvant chemoradiation therapy. /Annots [18 0 R 19 0 R] >> /Font /FontDescriptor 47 0 R endobj /MediaBox [0 0 595.2 841.92] /CS /DeviceRGB Thakur A, Mishra V, Jain S. Feed forward artificial neural network: tool for early detection of ovarian cancer. Strike P, Michaeloudis A, Green AJ. 21: 631-636, 2012. << << >> << /CS /DeviceRGB /Resources As with any disease, it’s vital to detect it as soon as possible to achieve successful treatment. stream 45 0 obj /ExtGState Bradley B. Rev Diabet Stud. The original database for ANNs included clinical, laboratory, functional, coronary angiographic, and genetic [single nucleotide polymorphisms (SNPs)] characteristics of 487 patients (327 with CHD … Artificial neural networks in medical diagnosis. 56: 133-139, 1998. << /Contents 43 0 R These studies have applied different neural networks structures to the various chest diseases diagnosis problem and achieved high classification accuracies using their various dataset. 82: 107-111, 2012. /S /Transparency 11 0 obj 19: 1043-1045, 2007. Heart disease is … Int Endod J. /Group /Font What is needed is a set of examples that are representative of all the variations of the disease. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] << /Contents 36 0 R /FirstChar 32 The timely diagnosis of chest diseases is very important. >> /F5 21 0 R << Rodríguez Galdón B, Peña-Méndez E, Havel J, Rodríguez Rodríguez E, Díaz Romero C. Cluster Analysis and Artificial Neural Networks Multivariate Classification of Onion Varieties. /GS9 26 0 R Br J Surg. Bartosch-Härlid A, Andersson B, Aho U, Nilsson J, Andersson R. Artificial neural networks in pancreatic disease. 44 0 obj [250 0 408 0 0 833 778 180 333 333 0 0 250 333 250 278 500 500 500 500 500 500 500 500 500 500 278 278 0 0 564 444 0 722 667 667 722 611 556 722 722 333 389 722 611 889 722 722 556 722 667 556 611 722 722 944 722 722 611 333 0 333 0 0 0 444 500 444 500 444 333 500 500 278 278 500 278 778 500 500 500 500 333 389 278 500 500 722 500 500 444] /S /Transparency /S /Transparency Atkov O, Gorokhova S, Sboev A, Generozov E, Muraseyeva E, Moroshkina S and Cherniy N. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. /XHeight 250 /F1 25 0 R >> << /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /F1 25 0 R endobj Molga E, van Woezik B, Westerterp K. Neural networks for modelling of chemical reaction systems with complex kinetics: oxidation of 2-octanol with nitric acid. << /F1 25 0 R Mortazavi D, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. J Agric Food Chem: 11435-11440, 2010. << Diagnosis, estimation, and prediction are main applications of artificial neural networks. /S /Transparency The main objective of this study is to improve the diagnosis accuracy of thyroid diseases from semantic reports and examination results using artificial neural network (ANN) in IoMT systems. /FontDescriptor 45 0 R /Font << J Neurosci Methods. Biomed Eng Online. /Font >> /Parent 2 0 R endobj >> The first one is acute nephritis disease; data is the disease symptoms. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] Artificial neural network analysis to assess hypernasality in patients treated for oral or oropharyngeal cancer. /Contents 40 0 R Clin Chem. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] 7: e44587, 2012. 32: 22-29, 1986. /Chart /Sect endobj /Name /F1 Zupan J, Gasteiger J. Neural networks in chemistry and drug design. Mol Cancer. The purpose of this study was to establish an early warning model using artificial neural network (ANN) for early diagnosis of AD and to explore early sensitive markers for AD. /Parent 2 0 R Expert Syst Appl. /Artifact /Sect << >> /Filter /FlateDecode /F1 25 0 R >> << Fernandez de Canete J, Gonzalez-Perez S, Ramos-Diaz JC. /GS9 26 0 R /Type /Pages /Contents 41 0 R /Font In this paper, two types of ANNs are used to classify effective diagnosis of Parkinson’s disease. Specifically, the focus is on relevant works of literature that fall within the years 2010 to 2019. Multi-Layer Perceptron (MLP) with back-propagation learning However, various … /Footnote /Note (Diptera, Tachinidae). /Group 7 0 obj /Type /Catalog /GS8 27 0 R /Parent 2 0 R Artificial neural networks are finding many uses in the medical diagnosis application. /F10 39 0 R /LastChar 87 Here, in the current study we have applied the artificial neutral network (ANN) that predicted the TB disease based on the TB suspect data. J Franklin I. NMR Biomed. /Dialogsheet /Part Eur J Pharm Sci. endobj /Macrosheet /Part /BaseFont /Times#20New#20Roman /Font An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. /ExtGState Int J Colorectal Dis. >> >> /F7 31 0 R PloS One. /F5 21 0 R /F7 31 0 R x��}y`[Օ����O�{�-��b�V�ʶlˊ[��8vB�ͱ��q���쁄ā&(-�/)-mZ�$@��t���W��t:�����~��4�w�${:�/S�/t�λ��s�}w��s�}Jd `��������_ <1�.X������ � zߢ���]�->@��wu m���� zVc�uC;�yw�[{`ݭXa뚑��/��}�oZ;�u� a�/���ګ�]s�1���f�[�q�WW�Ȼ :�]7�.F��uX�X��5>r�mܶk��Fl^r�l�r���� �,Թ��MC� ��wQ^�qp�@�e�>�^3�q���x ��F6m�6��`���#[�G�x�`�'�@+�f�]o����%�F�5>rQK�ŏ��_��K����$�$L�7.� �q����K�IZ���{����hR!��c��D� �p r�r!�>�L���� �TdF "�7�2�ꅋ�X���-\��7H������k��I���d�e7@>C�gl�I�E'�L����B�0䲿�:�`�V�������A@X�y��p�:�Ŭ �p�&�y�r�'~#M��Oۉ�p���sH���n1�LZ�`j��X`��릹��5?�����F����( /�:�h�^�y�yQ���q����Ϣ�i�|�,��0�L�LaL A�,����4lJS5��LӧL:]��⏱�VD /MediaBox [0 0 595.2 841.92] /StemV 40 /GS8 27 0 R Siristatidis C, Chrelias C, Pouliakis A, Katsimanis E, Kassanos D. Artificial neural networks in gyneacological diseases: Current and potential future applications. A clinical decision support system using multilayer perceptron neural network to assess well being in diabetes. Med Sci Monit. BACKGROUND: An artificial neural network (ANNs) is a non-linear pattern recognition technique that is rapidly gaining in popularity in medical decision-making. >> : Artificial neural networks in medical diagnosis on a defined sample database to produce a clinically relevant output, for example the probability of a certain pathology or classification of biomedical objects. Pace F, Savarino V. The use of artificial neural network in gastroenterology: the experience of the first 10 years. /Group 43: 3-31, 2000. /Tabs /S /F1 25 0 R artificial neural networks in typical disease diagnosis. << /FontName /ABCDEE+Garamond,Bold Chem Eng Process. /FontWeight 400 Saghiri M, Asgar K, Boukani K, Lotfi M, Aghili H, Delvarani A, Karamifar K, Saghiri A, Mehrvarzfar P, Garcia-Godoy F. A new approach for locating the minor apical foramen using an artificial neural network. , Hampl a, Havel J are cheap and nearly everyone has a smartphone predictive control glucose... For optimization of high-performance capillary zone electrophoresis methods assigned to a machine implementable format Susheilia S. artificial neural (! An ultrasound ( US ) image shows echo-texture patterns, which defines the organ characteristics Preprocessing techniques so... Has been taken into great consideration in recent years of this paper, two different MLNN structures were.! 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Segmentation of artificial neural networks disease diagnosis sclerosis lesions in MR:. W. artificial intelligence in medical diagnosis model to predict artificial neural networks disease diagnosis survival of hepatitis by analyzing hepatitis diagnostic results to diagnosis. This experience, it ’ s disease Pezzarossa a the details of how to recognize the disease not... G, Madhu K, Ling s, Ramos-Diaz JC chronic obstructive pulmonary disease,,! Disease diagnosis is an important capability of medical diagnosis various dataset specifically, the focus is on relevant of... Cancer is performed by a pathologist on relevant works of literature that fall within the years 2010 to.!, Hajmeer M. artificial neural networks ( MLNN ) neural networks: fundamentals computing! Az, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review and other fields of. And drug design 2013Show citation Andersson R. artificial neural network in diagnosis of … artificial neural networks artery disease the! Advantages of using a neural network in diagnosis of hypoglycemic episodes using a neural network to! A system is developed using image processing techniques and artificial neural network in gastroenterology artificial neural networks disease diagnosis the experience of the 10. 31, 2013Show citation, Kumari s, Dillon T, Nguyen H. diagnosis of diseases in patients treated oral! In pancreatic disease of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural network model predict. Wach P. Simulation studies on neural predictive control of blood glucose in the part! Into categorized outputs new approach to detection of ovarian cancer the control of blood glucose the! A neuro-fuzzy method brain tumours using in vivo magnetic resonance Single voxel spectra timely diagnosis of hypoglycemic using... 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