FUNDAMENTAL STEPS IN ARTIFICIAL NEURAL NETWORKS-BASED MEDICAL DIAGNOSIS

An Academic presentation byDr. Nancy Agens, Head, Technical Operations, Pubrica Group: www.pubrica.comEmail: sales@pubrica.com

In-Brief IntroductionSteps Involved in an Artificial Neural Network Features selectionLimitations Tools usedBuilding the databaseTraining and verification of database using artificial neural network Testing in Medical PracticeConclusion

Outline

Today's Discussion

An extensive source of information is available to all the medical professionals 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 Reviewing Services.The process of performing an artificial neural network for medical analysis must be appropriate and relevant. Pubrica briefly explains the abilities, procedure,

limitations of artificial neural networks in medical diagnosis.

In-Brief

IntroductionThe process of artificial neural network analysis rises from the clinical situations that give a brief overview of the purposes of medical diagnosis with enormous confidence.

Pre peer review for the neural network observes the patient's data to detect a specific disease.

Once after finding the target, the next step is to proceed the experiment with laboratory and instrumentation processes that give information about the patient's health conditions.

Contd..

There are many ways to perform these processes.

Many tools are there to perform the tasks.

However, careful selection of tools is essential to avoid noise-based instruments in the first stage.

The next stage is to formulate a database and to validate it.

Likewise, there few significant steps involved in performing artificial neural network analysis, and this blog explains the steps in the artificial neural network using peer review service.

Steps Involved in an Artificial Neural Network

Features Selection

Building the Database- Data cleaning and preprocessing

- Data homoscedasticity

Training and Verification of Database using Artificial

Neural Network

- Network type architecture

- Training algorithm

- Verification

-Robustness of artificial neural network-based approaches

Testing in Medical Practice

Diagnosis ofany specific disease depends on various data.

Features Selection

Medical professionals will extract the relevant data from each type of information to detect the most comfortable diagnosis.

For artificial neural network analysis, a collection of data is known as 'Features' that can be symptoms, phytochemical analysis, or any other relevantinformation helps for diagnostic purposes.

Contd..

So it is closely related to the final diagnosis.

The significance of Artificial neural networkis to grasp from previous samples, makes them very flexible and potential tools to perform medical diagnosis.

Few types of the artificial neural networkare acceptable for solving problems while other data modelling process are more efficient in approximation.

Robust indicators should help train neural networks using the clinical situationor pathology.

There are a few limitations for features before the selection processes given by peer review report.

Limitations

Insufficient Non-specific

Noisy information about the problem Redundant

The selection of appropriate features is essential for medical diagnosis using different medical approaches with the help of medical peer review services.

Tools Used

Mathematical means of data

Genetic algorithm

Principal component analysis

Building the Database

The "example" cases are a unified database for the neural network is training.

An "example" provides patient values for the selection, collection and evaluation.

The training quality and resultant generalization, the prediction capability of the network, firmly based on the training database.

The database must have enough number of relevant "examples" and allow the system to learn extracting the structure hidden in the dataset.

Also, clinical laboratory data must be in a document that is rapidly transferable to programs in computer-aided diagnosis using peer-reviewed articles.

Preprocess the training database before theevaluation process by the neural network.

Scale the data between the interval for the logisticpurpose.

Besides, the demonstration of a few cases,

some data are missing and remove it from the database set toimprove the performance of the n eural network.

There occurs a decrease in the performance of the system for imbalanced databases.

Contd..

1. Data Cleaning And Preprocessing

Evaluation of a training network for new patients by

The suitable features DatabaseData preprocessing method Training algorithmNetwork architecture Data concerningHomoscedasticity may lead to failure and misclassify the original data.

To overcome this problem, us additional parameters that belong to a particular sample indicating the population.

Contd..

2. Data Homoscedasticity

Training and Verification of Database using Artificial Neural Network

There are many other network models such asBayesian, recurrent, or fuzzy, stochastic but multilayer feed-forward neural networks are most common.

The optimal network architecture selection is the first stage.

The testing networks using a various number of hidden layers and nodes use them.

It gives the optimal architecture for which the minimum value of E for both training and verification.

NETWORK TYPE AND ARCHITECTURE

TRAINING ALGORITHM

Different types of training algorithms are available."Network learning" section, use of two training parameters:

(i) learning rate(ii) momentum.

VERIFICATION

Verification of dataset from various data for training for the a rtificial neural networks- based medical diagnosis is there in the process.

ROBUSTNESS OF ARTIFICIAL NEURAL NETWORK-BASED APPROACHES

The artificial neural network can tolerate a level of noise in the data. Consequently, they give sufficient prediction accuracy.

This noise may sometimes cause false results, mainly when modelling a complicated system like the health condition of a human body.

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 network systems having a peer-reviewed publication.

Testing in Medical Practice

The final step in the artificial neural network-aided diagnosis is testing medical practice.

Medical data of patients must be correct when including it in the training database.

The comprehensive and extensive evaluation of ANN diagnosis applications in the clinical sector is necessary for different institutions.

Only verified ANN medical diagnosis applications in the clinical industry are an essential condition for future expansion in medicine.

ConclusionThe ANN is a powerful tool for physicians to perform diagnosis.

It has several advantages, like processing a large amount of data and providing relevant information.

They make the diagnosis process more accessible and more straightforward.

Pubrica guides you to make use of the technologies wisely in this fast-emerging world.

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