Fundamental steps inartificial neural networks-based medical diagnosis – Pubrica

1. FUNDAMENTAL STEPS IN ARTIFICIAL NEURAL NETWORKS-BASED MEDICALDIAGNOSIS An Academic presentationby Dr.NancyAgens,Head,TechnicalOperations,Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion Outline In-Brief Introduction Steps Involved in an Artificial Neural Network Featuresselection Limitations Toolsused Building thedatabase Training and verification of database using artificial neural network Testing in MedicalPractice Conclusion

3. In-Brief An extensive source of information is available to all the medicalprofessionals starting from symptoms to biochemical analysis using imaging devices. The artificial neural network is an AI-based medical diagnostic tool used to evaluate the vast amount of data says medical Manuscript Peer ReviewingServices. The process of performing an artificial neural network for medical analysismust be appropriate and relevant. Pubrica briefly explains the abilities,procedure, limitations of artificial neural networks in medicaldiagnosis.

4. Introduction The process of artificial neural network analysis rises from the clinical situations that give a brief overview of the purposes of medical diagnosiswith enormousconfidence. Pre peer review for the neural network observesthe patient's data to detect a specificdisease. Once after finding the target, the next step is to proceed the experiment with laboratory and instrumentation processes that giveinformation about the patient's healthconditions. Contd..

5. There are many ways to perform theseprocesses. Many tools are there to perform thetasks. However, careful selection of tools is essential to avoid noise-basedinstruments in the firststage. The next stage is to formulate a database and to validateit. Likewise, there few significant steps involved in performing artificial neural network analysis, and this blog explains the steps in the artificial neuralnetwork using peer reviewservice.

6. FeaturesSelection • Building theDatabase • Data cleaning andpreprocessing • Datahomoscedasticity • Training and Verification of Database usingArtificial NeuralNetwork • Network typearchitecture • Trainingalgorithm • Verification • Robustness of artificial neural network-basedapproaches Testing in MedicalPractice Steps Involved in anArtificial Neural Network

7. Features Selection Diagnosis ofany specific disease dependson variousdata. Medical professionals will extract the relevant data from each type of information to detect the most comfortablediagnosis. For artificial neural network analysis, a collection of data is known as 'Features' that can besymptoms, phytochemical analysis, or any otherrelevant information helps for diagnosticpurposes. Contd..

8. So it is closely related to the finaldiagnosis. The significance of Artificial neural networkis to grasp from previoussamples, makes them very flexible and potential tools to perform medicaldiagnosis. Few types of the artificial neural networkare acceptable for solving problemswhile other data modelling process are more efficient inapproximation. Robust indicators should help train neural networks using the clinicalsituation orpathology. There are a few limitations for features before the selection processes givenby peer review report.

9. Limitations Insufficient Non-specific Noisy information about theproblem Redundant The selection of appropriate features is essential formedical diagnosis using different medical approaches with the help ofmedical peer reviewservices.

10. ToolsUsed Mathematical means ofdata Geneticalgorithm Principal componentanalysis

11. Buildingthe Database The "example" cases are a unified database forthe neural network istraining. An "example" provides patient values fortheselection, collection andevaluation. The training quality and resultant generalization, the prediction capability of the network, firmly based onthe trainingdatabase.

12. The database must have enough number of relevant "examples" and allow the system to learn extracting the structure hidden in thedataset. Also, clinical laboratory data must be in a document that is rapidlytransferable to programs in computer-aided diagnosis using peer-reviewedarticles.

13. 1. DataCleaning And Preprocessing Preprocess the training database beforethe evaluation process by the neuralnetwork. Scale the data between the interval for thelogistic purpose. Besides, the demonstration of a few cases, somedata are missing and remove it from the database setto improve the performance of the neuralnetwork. There occurs a decrease in the performance ofthe system for imbalanceddatabases. Contd..

14. 2. Data Homoscedasticity Evaluation of a training network for new patientsby The suitablefeatures Database Data preprocessingmethod Trainingalgorithm Networkarchitecture Dataconcerning Homoscedasticity may lead to failure andmisclassify the originaldata. To overcome this problem, us additional parametersthat belong to a particular sample indicating thepopulation. Contd..

16. Training and Verification of Database usingArtificial Neural Network NETWORK TYPE ANDARCHITECTURE There are many other network models suchas Bayesian, recurrent, or fuzzy, stochastic butmultilayer feed-forward neural networks are mostcommon. The optimal network architecture selection isthe firststage. The testing networks using a various numberof hidden layers and nodes usethem. It gives the optimal architecture for which the minimum value of E for both training andverification.

17. TRAINING ALGORITHM Different types of training algorithms are available."Network learning" section, use of two trainingparameters: learningrate momentum. VERIFICATION Verification of dataset from various data for training for the artificial neuralnetworks- based medical diagnosisis there in theprocess.

18. ROBUSTNESS OF ARTIFICIAL NEURAL NETWORK-BASEDAPPROACHES The artificial neural network cantolerate a level of noise in thedata. Consequently, they give sufficient predictionaccuracy. This noise may sometimes cause false results, mainly when modellinga complicated system like the health condition of a humanbody. One of the best ways to avoid this is to perform the process by an experienced clinician knowing the discriminative power of the artificial neural networksystems having a peer-reviewed publication.

19. Testingin Medical Practice The final step in the artificial neuralnetwork-aided diagnosis is testing medicalpractice. Medical data of patients must be correct when including itin the training database. The comprehensive and extensive evaluation of ANN diagnosis applications in the clinical sector is necessaryfor differentinstitutions. Only verifiedANN medical diagnosisapplications inthe clinical industry are an essential condition for future expansion inmedicine.

20. Conclusion The ANN is a powerful tool for physiciansto performdiagnosis. It has several advantages, like processing a large amount of data and providing relevantinformation. They make the diagnosis process moreaccessible and morestraightforward. Pubrica guides you to make use of thetechnologies wisely in this fast-emergingworld.

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