Convolutional Neural Networks (CNN) can be used as an efficient tool for detecting diseases between different types of medical imaging in a fast and reliable way, so that the article focuses on pneumonia disease, as yearly about 2.56 million people die in consequence of this illness. This paper illustrate the importance of data preprocessing as an effective approach for producing better results since raw data repercute in the training time, in consequence, it require more computational time to complete a determined machine learning problem. One of the main focal point is to introduce a novel and effective method to work with large amount of data and how it can be preprocessed for getting almost ideal results with a minimal lost of information due preprocessing. As the main result, the solution mentioned can help radiology and medical personnel to diagnose X-Ray images. Regarding the dataset, its name is Chest X-Ray Image Dataset, it's a public dataset of Kaggle and contains 5856 JPEG images organized in three directories. As future work, this model can be used to work with other types of Medical Images due to its adaptability.
|Title of host publication||2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 4 Nov 2020|
|Event||2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020 - Ixtapa, Mexico|
Duration: 4 Nov 2020 → 6 Nov 2020
|Name||2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020|
|Conference||2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020|
|Period||4/11/20 → 6/11/20|
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© 2020 IEEE.
- Artificial neuron