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IMPROVED MEDICAL DIAGNOSIS OF CHEST X-RAYS USING DEEP LEARNING WITH INCREMENTAL ITERATIONS

SEPTEMBER 2022   -  Volume: 97 -  Pages: 522-527

DOI:

https://doi.org/10.6036/10542

Authors:

ARON HERNANDEZ TRINIDAD
- EDUARDO PEREZ CARETA - ANGELICA HERNANDEZ RAYAS -
TEODORO CORDOVA FRAGA
-
RAFAEL GUZMAN CABRERA

Disciplines:

  • Computer Sciences (ARTIFICIAL INTELLIGENCE / INTELIGENCIA ARTIFICIAL )

Downloads:   14

How to cite this paper:  

Received Date :   30 March 2022

Reviewing Date :   1 April 2022

Accepted Date :   23 June 2022

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Key words:
Deep learning, improving medical diagnosis, automatic image classification, aprendizaje automático, inteligencia artificial, redes neuronales, procesamiento de imágenes
Article type:
ARTICULO DE INVESTIGACION / RESEARCH ARTICLE
Section:
RESEARCH ARTICLES

Pneumonia is an inflammatory condition of the lung that affects the alveoli. Diagnosis is based on symptoms and physical examination. Chest radiographs are used as an alternative to validate the diagnosis. In the present work, a methodology is presented to perform image processing based on machine learning and artificial intelligence to perform an automatic classification of said images. Results of experiments carried out in two classification scenarios are presented: cross-validation and training and test sets. Five different machine learning methods were used in each classification scenario, as well as five evaluation metrics. Similarly, the images were preprocessed with five filters, in addition to the original images. The oriented gradient histogram feature descriptor was used to measure the effectiveness in both cases: original and with filters. The configuration of the experiment was planned in such a way that it allowed to identify the best classification conditions, also allowing to clearly observe the impact of the size of the training set on the evaluation metrics used. The results obtained allow us to see the effectiveness of the implemented methodology, since the results are competitive with those reported in the state of the art.

Keywords: machine learning, artificial intelligence, neural networks, image processing.

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