/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] >> 21: 427-436, 2008. Eur J Pharm Sci. 59: 190-194, 2012. /F1 25 0 R >> Heart disease is … << /Contents 42 0 R 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. endobj Artificial neural network is a technique which tries to simulate behavior of the neurons in humans’ brain. Thakur A, Mishra V, Jain S. Feed forward artificial neural network: tool for early detection of ovarian cancer. /S /Transparency Neuroradiology. Med Sci Monit. Artificial neural networks with their own data try to determine if a /Type /Group /MediaBox [0 0 595.2 841.92] /Flags 32 /GS8 27 0 R 93: 72-78, 2012. 4 0 obj 2013;11(2):47-58. doi: 10.2478/v10136-012-0031-x. 79: 493-505, 2011. endobj 4: 29, 2005. /Type /Catalog For detecting crop disease early and accurately, a system is developed using image processing techniques and artificial neural network. /Diagram /Figure /F1 25 0 R << Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes. << << 8: 1105-1111, 2008. Biomed Eng Online. 349: 1851-1870, 2012. This study demonstrated the ability of an artificial neural network to predict patient survival of hepatitis by analyzing hepatitis diagnostic results. /FontFile2 48 0 R /Resources /ItalicAngle 0 /Group << /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] 108: 80-87, 1988. Artificial neural networks combined with experimental design: a "soft" approach for chemical kinetics. /Font Artificial Neural Network (ANN) techniques to the diagnosis of diseases in patients. >> /Group /Contents 38 0 R Tuberculosis Disease Diagnosis Using Artificial Neural Networks. /Resources endobj /Resources /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] << For this purpose, two different MLNN structures were used. 24: 401-410, 2005. >> /CS /DeviceRGB >> 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. /GS8 27 0 R 15: 80-87, 2001. de Bruijn M, ten Bosch L, Kuik D, Langendijk J, Leemans C, Verdonck-de Leeuw I. /Type /Group 59: 190-194, 2012. /FontBBox [-147 -263 1168 654] << /S /Transparency Breast cancer is a widespread type of cancer (for example in the UK, it’s the most common cancer). /CS /DeviceRGB Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. Int Endod J. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] << Artificial neural networks are finding many uses in the medical diagnosis application. << /S /Transparency The training phase is the critical part of the process and need the availability of data of healthy and damaged cases. PloS One. << Comput Meth Progr Biomed. >> << Standardizing clinical laboratory data for the development of transferable computer-based diagnostic programs. /StructParents 9 << /Resources The system can be deployed in smartphones, smartphones are cheap and nearly everyone has a smartphone. /GS8 27 0 R Ecotoxicology. /Type /Font /S /Transparency Earlier diagnosis of hypertension saves enormous lives, failing which may lead to other sever problems causing sudden fatal end. Due to the substantial plasticity of input data, ANNs have proven useful in the analysis of blood Artificial Neural Network can be applied to diagnosing breast cancer. 21: 631-636, 2012. Alkim E, Gürbüz E, Kiliç E. A fast and adaptive automated disease diagnosis method with an innovative neural network model. Curr Opin Biotech. /Parent 2 0 R For this purpose, a probabilistic neural network structure was used. /Type /FontDescriptor >> Int Thomson Comput Press, London 1995. Through this experience, it appears that deep learning can provide significant help in the field of medicine and other fields. As with any disease, it’s vital to detect it as soon as possible to achieve successful treatment. /ItalicAngle 0 /Type /Page /XHeight 250 /S /Transparency J Cardiol. 36: 168-174, 2011. /MediaBox [0 0 595.2 841.92] /F9 29 0 R /F7 31 0 R 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. >> /Textbox /Sect << 8 0 obj /MediaBox [0 0 595.2 841.92] 2012. >> 43: 3-31, 2000. J Diabet Complicat. >> /Resources 34: 299-302, 2008. /Name /F2 : 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. /GS8 27 0 R 1 0 obj << Here, in the current study we have applied the artificial neutral network (ANN) that predicted the TB disease based on the TB suspect data. /RoleMap 17 0 R /Font /Length 21590 Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. Nowadays, one of the main issues to create challenges in medicine sciences by developing technology is the disease diagnosis with high accuracy. /StructParents 5 /F10 39 0 R /FontWeight 400 /Font >> /Group /CS /DeviceRGB The second is the heart disease; data is on cardiac Single Proton Emission Computed Tomography (SPECT) images. /Type /Page /Tabs /S /F7 31 0 R /Flags 32 /Tabs /S /F7 31 0 R /ExtGState /Descent -263 J Appl Biomed. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] 2011: 158094, 2011. artificial neural networks in typical disease diagnosis. /F8 30 0 R /F6 20 0 R /GS9 26 0 R One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. << >> /Tabs /S << << In the recent decades, Artificial Neural Networks (ANNs) are considered as the best solutions to achieve /MaxWidth 1315 /GS9 26 0 R /F7 31 0 R /Contents 34 0 R 23: 1323-1335, 2002. Tuberculosis is important health problem in Turkey also. Two cases are studied. /S /Transparency /Group Havel J, Peña E, Rojas-Hernández A, Doucet J, Panaye A. Neural networks for optimization of high-performance capillary zone electrophoresis methods. >> 91: 1615-1635, 2001. 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. Chest diseases are very serious health problems in the life of people. /Tabs /S << 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 Eur J Surg Oncol. /BaseFont /ABCDEE+Garamond,Bold /F7 31 0 R /F6 20 0 R J Med Syst. 32: 22-29, 1986. Spelt L, Andersson B, Nilsson J, Andersson R. Prognostic models for outcome following liver resection for colorectal cancer metastases: A systematic review. >> >> WASET. Özbay Y. /GS8 27 0 R << /CS /DeviceRGB 95: 817-826, 2008. 7: e44587, 2012. /Resources Specifically, the focus is on relevant works of literature that fall within the years 2010 to 2019. /F1 25 0 R /Contents 40 0 R Amato F, González-Hernández J, Havel J. /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] >> 56: 133-139, 1998. In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). J Chromatogr A. /Type /FontDescriptor The control of blood glucose in the critical diabetic patient: a neuro-fuzzy method. /MediaBox [0 0 595.2 841.92] /Pages 2 0 R 6 0 obj /Dialogsheet /Part /MediaBox [0 0 595.2 841.92] >> /Font 35: 329-332, 2011. 95: 544-554, 2009. 12 0 obj endobj Dazzi D, Taddei F, Gavarini A, Uggeri E, Negro R, Pezzarossa A. /Tabs /S /Type /Group 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. Fernandez de Canete J, Gonzalez-Perez S, Ramos-Diaz JC. 57: 4196-4199, 1997. �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�. /F6 20 0 R In this paper, two types of ANNs are used to classify effective diagnosis of Parkinson’s disease. 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. Mol Cancer. << >> << /StructTreeRoot 3 0 R NMR Biomed. /Group 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. Bradley B. /F6 20 0 R /Type /Group /Footnote /Note << s A a classification system, ANNs are an important tool for decision- /XObject 38: 9799-9808, 2011. /StructParents 3 /MediaBox [0 0 595.2 841.92] /F7 31 0 R Many methods have been developed for this purpose. The System can be installed on the device. The timely diagnosis of chest diseases is very important. /AvgWidth 401 /F1 25 0 R Appl Soft Comput. /Type /Group 33: 335-339, 2012. /Parent 2 0 R << The role of computer technologies is now increasing in the diagnostic procedures. >> /Endnote /Note ;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. /Type /Page << Dayhoff J, Deleo J. Artificial neural networks are finding many uses in the medical diagnosis application. /Type /Page /Group /Name /F1 /Artifact /Sect endobj << /Contents 36 0 R /StructParents 0 /F5 21 0 R 54: 299-320, 2012a. 77: 145-153, 1994. /S /Transparency << Mortazavi D, Kouzani A, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. 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] /Group /ExtGState /Annots [18 0 R 19 0 R] /ExtGState J Cardiol. << >> Shankaracharya, Odedra D, Samanta S, Vidyarthi A. Computational intelligence in early diabetes diagnosis: a review. /F5 21 0 R The system for medical diagnosis using neural networks will help patients diagnose the disease without the need of a medical expert. 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. /F1 25 0 R Li Y, Rauth AM, Wu XY. /StructParents 10 /FontDescriptor 47 0 R /Type /Page /Type /Page 45: 257-265, 2012. /Type /Page << /ExtGState /Workbook /Document Pattern Recogn Lett. >> Bartosch-Härlid A, Andersson B, Aho U, Nilsson J, Andersson R. Artificial neural networks in pancreatic disease. 14 0 obj 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. /FirstChar 32 >> << << Br J Surg. >> >> >> >> (Diptera, Tachinidae). /Parent 2 0 R << endobj /GS9 26 0 R >> /F7 31 0 R Finding biomarkers is getting easier. << %PDF-1.5 J Med Syst. /Resources Fedor P, Malenovsky I, Vanhara J, Sierka W, Havel J. Thrips (Thysanoptera) identification using artificial neural networks. /GS8 27 0 R /StructParents 4 >> /Annotation /Sect 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. /Font /F6 20 0 R J Appl Biomed 11:47-58, 2013 | DOI: 10.2478/v10136-012-0031-x. Multi-Layer Perceptron (MLP) with back-propagation learning 54: 299-320, 2012b. BACKGROUND: An artificial neural network (ANNs) is a non-linear pattern recognition technique that is rapidly gaining in popularity in medical decision-making. Improving an Artificial Neural Network Model to Predict Thyroid Bending Protein Diagnosis Using Preprocessing Techniques. /Type /Pages << >> /Chart /Sect Chan K, Ling S, Dillon T, Nguyen H. Diagnosis of hypoglycemic episodes using a neural network based rule discovery system. Szolovits P, Patil RS, Schwartz W. Artificial Intelligence in Medical Diagnosis. Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural networks. << /S /Transparency >> >> 11: 3, 2012. >> /Lang (en-US) /Font Chem Eng Process. The results of the experiments and also the advantages of using a fuzzy approach were discussed as well. 33: 435-445, 2009. 33: 88-96, 2012. 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. /GS9 26 0 R endobj << /Resources The diagnosis of breast cancer is performed by a pathologist. /GS9 26 0 R /Parent 2 0 R /Font /StructParents 6 << 48 0 obj << /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /F8 30 0 R Neural networks. Of medicine and other fields diagnosis application the role of computer technologies is now in! Help in the UK, it ’ s disease technique which tries to simulate behavior of the was. 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