A New Scalable Deep Learning Model of Pattern Recognition for Medical Diagnosis Using Model Aggregation and Model Selection

In recent years, pattern recognition has become a research area with increasing importance using several techniques. One of the most common techniques used is deep learning. This paper presents a new deep learning model to pattern recognition for medical diagnosis. The uncovering of hidden structure...

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Bibliographic Details
Published in:International journal of information retrieval research 2023-07, Vol.12 (1), p.1-16
Main Authors: Adda, Mehdi, Djellali, Choukri
Format: Article
Language:English
Subjects:
Associative memory
Deep learning
Diagnosis
Machine learning
Neural networks
Pattern recognition
Radial basis function
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Summary:In recent years, pattern recognition has become a research area with increasing importance using several techniques. One of the most common techniques used is deep learning. This paper presents a new deep learning model to pattern recognition for medical diagnosis. The uncovering of hidden structures is performed by feature selection, model aggregation, and model selection. The deep learning model has the ability to reach the optimal solution and create complex decision boundaries when used to look for and diagnose breast cancer. The evaluation, based on 10-fold cross-validation, showed that the proposed model, which is named BaggingSMF, yielded good results and performed better than radial basis function, bidirectional associative memory, and ELMAN neural networks. Experimental studies demonstrate the multidisciplinary applications of the model.
ISSN:2155-6377
2155-6385
DOI:10.4018/IJIRR.316131