Elveren E, Yumuşak N. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. /Type /Group /Diagram /Figure >> 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. Brougham D, Ivanova G, Gottschalk M, Collins D, Eustace A, O'Connor R, Havel J. >> endobj /GS8 27 0 R Artificial Neural Network (ANN)-based diagnosis of medical diseases has been taken into great consideration in recent years. 19: 1043-1045, 2007. Two cases are studied. The second is the heart disease; data is on cardiac Single Proton Emission Computed Tomography (SPECT) images. Tuberculosis Disease Diagnosis Using Artificial Neural Networks. What is needed is a set of examples that are representative of all the variations of the disease. Cytometry B Clyn Cytom. /ExtGState The results of the study were compared with the results of the previous studies reported focusing on hepatitis disease diagnosis and using same UCI machine learning database. The results of the experiments and also the advantages of using a fuzzy approach were discussed as well. 38: 9799-9808, 2011. /GS8 27 0 R << /GS9 26 0 R << << /MarkInfo /Font /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /MediaBox [0 0 595.2 841.92] << /Resources /F10 39 0 R /StructParents 6 /Type /Group /GS9 26 0 R << /GS8 27 0 R In the recent decades, Artificial Neural Networks (ANNs) are considered as the best solutions to achieve /S /Transparency 16: 231-236, 2010. /Subtype /TrueType /Group Eur J Pharm Sci. Mortazavi D, Kouzani A, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. /F1 25 0 R endobj /CS /DeviceRGB /Parent 2 0 R /MediaBox [0 0 595.2 841.92] /S /Transparency /Parent 2 0 R 34: 299-302, 2008. >> << /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes. /GS8 27 0 R Özbay Y. Cancer. /GS8 27 0 R /MediaBox [0 0 595.2 841.92] >> 56: 133-139, 1998. >> /Worksheet /Part 11: 3, 2012. Er O, Temurtas F, Tanrikulu A. /F7 31 0 R /StructParents 4 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. Bartosch-Härlid A, Andersson B, Aho U, Nilsson J, Andersson R. Artificial neural networks in pancreatic disease. 11 0 obj For this purpose, two different MLNN structures were used. >> /GS8 27 0 R /Type /Page 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. /FontDescriptor 47 0 R 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. >> >> /Group 24: 401-410, 2005. >> Tate A, Underwood J, Acosta D, Julià-Sapé M, Majós C, Moreno-Torres A, Howe F, van der Graaf M, Lefournier V, Murphy M, Loosemore A, Ladroue C et al. /MaxWidth 1315 /Type /Page 2013;11(2):47-58. doi: 10.2478/v10136-012-0031-x. As with any disease, it’s vital to detect it as soon as possible to achieve successful treatment. In this paper, two types of ANNs are used to classify effective diagnosis of Parkinson’s disease. /StemV 42 << /F8 30 0 R Eur J Surg Oncol. /Font /Tabs /S WASET. /Type /Page 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. endobj >> /Type /Pages Int J Colorectal Dis. /Image34 33 0 R endobj 36: 61-72, 2012. 39: 323-334, 2000. endobj Curr Opin Biotech. 57: 127-133, 2009. >> Verikas A, Bacauskiene M. Feature selection with neural networks. >> << The timely diagnosis of chest diseases is very important. /Type /Group /Name /F2 Neuroradiology. 59: 190-194, 2012. Heart disease is … In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] Mol Cancer. /Descent -263 /F9 29 0 R /FontDescriptor 45 0 R Chem Eng Process. >> Neural networks. The role of computer technologies is now increasing in the diagnostic procedures. /Resources 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. 59: 190-194, 2012. /Chartsheet /Part >> /F8 30 0 R >> >> >> Bull Entomol Res. << /Leading 42 /Type /Page Aleksander I, Morton H. An introduction to neural computing. /Type /Group Alkim E, Gürbüz E, Kiliç E. A fast and adaptive automated disease diagnosis method with an innovative neural network model. /CS /DeviceRGB /MediaBox [0 0 595.2 841.92] J Franklin I. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. /Font /StructParents 0 /Tabs /S Chan K, Ling S, Dillon T, Nguyen H. Diagnosis of hypoglycemic episodes using a neural network based rule discovery system. /GS9 26 0 R Appl Soft Comput. << /StructParents 1 << /S /Transparency Breast cancer is a widespread type of cancer (for example in the UK, it’s the most common cancer). /S /Transparency 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. 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] /Type /Group 33: 335-339, 2012. endobj 54: 299-320, 2012a. artificial neural networks in typical disease diagnosis. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. %���� /Tabs /S >> /ExtGState /Name /F1 Ahmed F. Artificial neural networks for diagnosis and survival prediction in colon cancer. Dayhoff J, Deleo J. << /StructParents 2 Catalogna M, Cohen E, Fishman S, Halpern Z, Nevo U, Ben-Jacob E. Artificial neural networks based controller for glucose monitoring during clamp test. /Type /Font >> Artificial neural networks with their own data try to determine if a The control of blood glucose in the critical diabetic patient: a neuro-fuzzy method. /Contents 40 0 R 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. 7: 252-262, 2010. /Group /F1 25 0 R /Resources /F7 31 0 R /CS /DeviceRGB /Length1 55544 106: 55-66, 2012. /FontFile2 48 0 R /Endnote /Note These studies have applied different neural networks structures to the various chest diseases diagnosis problem and achieved high classification accuracies using their various dataset. 24 0 obj /GS8 27 0 R /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] endobj << 8: 1105-1111, 2008. >> >> >> /FontWeight 700 Eur J Gastroenterol Hepatol. << 95: 544-554, 2009. Int Endod J. << << 95: 817-826, 2008. /Tabs /S /CS /DeviceRGB /Type /Group Ecotoxicology. /Contents 36 0 R << >> Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. /ParentTreeNextKey 11 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. << Basheer I, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. 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. /F5 21 0 R J Med Syst. J Cardiol. Artificial neural network analysis to assess hypernasality in patients treated for oral or oropharyngeal cancer. /StemV 40 Wiley VCH, Weinheim, 380 p. 1999. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. This study investigated the use of ANNs for diagnostic and prognostic purposes in pancreatic disease, especially acute … /Tabs /S /Type /Group �NBL��( �T��5��E[���"�^Ұ)� NaSQ�I{�!��6�i���f��iJ�e�A/_6%���kؔD��%U��S5��LӧLF�X�g�|3bS'K��MɠG{)�N2L՜^C�i�Ĥ/�2�z��àR��Ĥ,�:9��4}��*z ���6u�3�d=bS'+FĤN��u�^eN�a��U��t�dR ��M=�z*�:UAl�%�A�L�Lc3M�2�MF�8N�A���z�c`jH`Ӥ��4Hz�^��9��46��ɒ��L�\^¦A1�T�&��A6 ����k�iߟ�4]6Y��e`� FըW�F�٤��^6*�T�46��)�͢j��� Naӈ�TIlZ�h/�j��9��46���n5��3a37A�0S� �b�Z4l��b��9����I�)M�M[���)l*��U� ��*6�rU�شM՜^C�i�Ĕa7_6UP-&Ō�qU�[ї��&�j����f�>er9� �2�87��l�����1������fΘ�9���ޗ�)M�M�. 13 0 obj 4 0 obj Tuberculosis is important health problem in Turkey also. << /StructParents 8 /Annots [18 0 R 19 0 R] [1] “Viral Hepatitis,” 2020. https://my.clevelandclinic.org/health/diseas es/4245-hepatitis-viral-hepatitis-a-b--c (accessed May 17, … /GS9 26 0 R 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. /StructTreeRoot 3 0 R endobj << << For detecting crop disease early and accurately, a system is developed using image processing techniques and artificial neural network. Standardizing clinical laboratory data for the development of transferable computer-based diagnostic programs. However, various … /Descent -216 Arnold M. Non-invasive glucose monitoring. /S /Transparency >> << Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural networks. Dazzi D, Taddei F, Gavarini A, Uggeri E, Negro R, Pezzarossa A. Sci Pharm. >> /Group /Artifact /Sect /FontBBox [-568 -216 2046 693] /Resources /Parent 2 0 R 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. >> >> Dey P, Lamba A, Kumari S, Marwaha N. Application of an artificial neural network in the prognosis of chronic myeloid leukemia. /ExtGState Pattern Recogn Lett. /Type /Catalog /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] endobj /F1 25 0 R /Font Comput Meth Progr Biomed. /F8 30 0 R 57: 4196-4199, 1997. /F1 25 0 R Cancer Lett. >> 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. /F1 25 0 R Finding biomarkers is getting easier. /Resources 108: 80-87, 1988. 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. endobj /F5 21 0 R s A a classification system, ANNs are an important tool for decision- << << /FontName /Times#20New#20Roman 98: 437-447, 2008. /GS9 26 0 R Szolovits P, Patil RS, Schwartz W. Artificial Intelligence in Medical Diagnosis. The diagnosis of breast cancer is performed by a pathologist. /Type /Group /F6 20 0 R 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. /Font /F1 25 0 R Logoped Phoniatr Vocol. This technique has had a wide usage in recent years. ;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��)/'$I�� N��pfg:\T:3r. << /Slide /Part Mortazavi D, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. /F9 29 0 R /F1 25 0 R 2 0 obj Artificial Neural Network (ANN) techniques to the diagnosis of diseases in patients. Specifically, the focus is on relevant works of literature that fall within the years 2010 to 2019. x��}y`[Օ����O�{�-��b�V�ʶlˊ[��8vB�ͱ��q���쁄ā&(-�/)-mZ�$@��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 /Contents 34 0 R J Med Syst. The original database for ANNs included clinical, laboratory, functional, coronary angiographic, and genetic [single nucleotide polymorphisms (SNPs)] characteristics of 487 patients (327 with CHD … /Resources 33: 435-445, 2009. /F6 20 0 R /XHeight 250 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. >> 23: 1323-1335, 2002. J Neurosci Methods. Artificial neural networks are finding many uses in the medical diagnosis application. Artificial neural networks in medical diagnosis. endobj << /MediaBox [0 0 595.2 841.92] 77: 145-153, 1994. /F5 21 0 R /F1 25 0 R Wilding P, Morgan M, Grygotis A, Shoffner M, Rosato E. Application of backpropagation neural networks to diagnosis of breast and ovarian cancer. 7: e44587, 2012. << Artificial neural networks (ANNs) are a mathematics based computational model which is used in computer sciences and other research disciplines, which is based on a large collection of simple units called artificial neurons, vaguely similar to the noticed behavior changes or … Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance. Biomed Eng Online. Havel J, Peña E, Rojas-Hernández A, Doucet J, Panaye A. Neural networks for optimization of high-performance capillary zone electrophoresis methods. << /GS9 26 0 R Zupan J, Gasteiger J. Neural networks in chemistry and drug design. 6 0 obj endobj /F2 24 0 R >> << /Font 12 0 obj /Parent 2 0 R /Tabs /S >> endobj << /BaseFont /ABCDEE+Garamond,Bold (Diptera, Tachinidae). 14 0 obj J Microbiol Meth. endobj /Tabs /S /Group >> The System can be installed on the device. /ExtGState 79: 493-505, 2011. /Contents 32 0 R /LastChar 122 endobj << /GS9 26 0 R /Workbook /Document /ExtGState /StructParents 7 >> Diagnosis, estimation, and prediction are main applications of artificial neural networks. /Annotation /Sect /LastChar 87 /F5 21 0 R /GS8 27 0 R 17 0 obj /RoleMap 17 0 R /Ascent 891 Talanta. 54: 299-320, 2012b. /ItalicAngle 0 << /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] Comput Meth Progr Biomed. 25 0 obj >> << Neuroradiology. Fernandez de Canete J, Gonzalez-Perez S, Ramos-Diaz JC. /CS /DeviceRGB /Group << 45: 257-265, 2012. Karabulut E, Ibrikçi T. Effective diagnosis of coronary artery disease using the rotation forest ensemble method. /Widths 46 0 R << 32: 22-29, 1986. The real procedure of medical diagnosis which usually is employed by physicians was analyzed and converted to a machine implementable format. Li Y, Rauth AM, Wu XY. /AvgWidth 401 /Footnote /Note In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples. >> /Contents 28 0 R /Type /Page << /Contents 37 0 R /Type /Group Artificial neural network is a technique which tries to simulate behavior of the neurons in humans’ brain. /Flags 32 43: 3-31, 2000. << /Footer /Sect Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. /CS /DeviceRGB /F8 30 0 R /Resources J Med Syst. %PDF-1.5 /MediaBox [0 0 595.2 841.92] /Contents 38 0 R /Resources /Type /Page >> NMR Biomed. /Contents 35 0 R Med Sci Monit. >> /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] Amato F, González-Hernández J, Havel J. Ann Intern Med. Artificial neural networks combined with experimental design: a "soft" approach for chemical kinetics. 38: 16-24, 2012. /Group /Pages 2 0 R >> /Parent 2 0 R /Subtype /TrueType /S /Transparency << /Font << /MediaBox [0 0 595.2 841.92] << >> << >> /CapHeight 693 /F7 31 0 R two artificial neural networks created for the diagnosis of diseases in fish caused by protozoa and bacteria. /Font << >> /F7 31 0 R << 7: 46-49, 1996. /ExtGState J Chromatogr A. /F7 31 0 R /Type /FontDescriptor >> /Tabs /S << /Font /K [15 0 R] These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. /Encoding /WinAnsiEncoding << J Appl Biomed 11:47-58, 2013 | DOI: 10.2478/v10136-012-0031-x. J Med Syst. /XObject /Parent 2 0 R /GS8 27 0 R The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. << Anal Quant Cytol Histol. /ItalicAngle 0 /F7 31 0 R endobj /FontWeight 400 << /Resources 1 0 obj Artificial Neural Network can be applied to diagnosing breast cancer. 36: 3011-3018, 2012. A new approach to detection of ECG arrhythmias: Complex discrete wavelet transform based complex valued artificial neural network. >> /F1 25 0 R endobj /S /Transparency >> /InlineShape /Sect /Type /Page >> /MediaBox [0 0 595.2 841.92] << /Type /Group 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. << J Biomed Biotechnol. J Parasitol. /Widths 44 0 R /Group /Type /Page >> Shankaracharya, Odedra D, Samanta S, Vidyarthi A. Computational intelligence in early diabetes diagnosis: a review. 82: 107-111, 2012. Trajanoski Z, Regittnig W, Wach P. Simulation studies on neural predictive control of glucose using the subcutaneous route. 21: 631-636, 2012. /MaxWidth 2614 /Tabs /S /Tabs /S /Type /Page >> >> Yan H, Zheng J, Jiang Y, Peng C, Xiao S. Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm. /CS /DeviceRGB /Length 21590 209: 410-419, 2012. >> >> >> Murarikova N, Vanhara J, Tothova A, Havel J. Polyphasic approach applying artificial neural networks, molecular analysis and postabdomen morphology to West Palaearctic Tachina spp. In the paper, convolutional neural networks (CNNs) are pre… Through this experience, it appears that deep learning can provide significant help in the field of medicine and other fields. << In this study, a comparative hepatitis disease diagnosis study was realized. The system can be deployed in smartphones, smartphones are cheap and nearly everyone has a smartphone. /S /Transparency Due to the substantial plasticity of input data, ANNs have proven useful in the analysis of blood PloS One. 48 0 obj << 8 0 obj >> /Count 11 /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] >> Pace F, Savarino V. The use of artificial neural network in gastroenterology: the experience of the first 10 years. Neural networks learn by example so the details of how to recognize the disease are not needed. Strike P, Michaeloudis A, Green AJ. Thakur A, Mishra V, Jain S. Feed forward artificial neural network: tool for early detection of ovarian cancer. /Parent 2 0 R /Type /StructTreeRoot J Cardiol. 19: 411-434, 2006. /F6 20 0 R 91: 1615-1635, 2001. /Flags 32 /Resources /Filter /FlateDecode /CS /DeviceRGB This study demonstrated the ability of an artificial neural network to predict patient survival of hepatitis by analyzing hepatitis diagnostic results. 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. 2012. /MediaBox [0 0 595.2 841.92] Fedor P, Malenovsky I, Vanhara J, Sierka W, Havel J. Thrips (Thysanoptera) identification using artificial neural networks. /F6 20 0 R Amato et al. /GS9 26 0 R /CS /DeviceRGB Earlier diagnosis of hypertension saves enormous lives, failing which may lead to other sever problems causing sudden fatal end. Background Alzheimer’s disease has become a public health crisis globally due to its increasing incidence. /MediaBox [0 0 595.2 841.92] Siristatidis C, Chrelias C, Pouliakis A, Katsimanis E, Kassanos D. Artificial neural networks in gyneacological diseases: Current and potential future applications. 3 0 obj J Assoc Physicians India. Spelt L, Andersson B, Nilsson J, Andersson R. Prognostic models for outcome following liver resection for colorectal cancer metastases: A systematic review. 21: 427-436, 2008. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] 9 0 obj /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] 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. << /Marked true /Parent 2 0 R /Type /Font /StructParents 10 endobj << << /F8 30 0 R /S /Transparency << << Mazurowski M, Habas P, Zurada J, Lo J, Baker J, Tourassi G. 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