<abstract xmlns="http://www.w3.org/1999/xhtml"><p>According to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in healthcare domain. As there are lots of subdomains which come under the category of healthcare domain, the proposed paper concentrates on one such domain, that is breast cancer and pneumonia. Today, just classifying the diseases is not enough. The system should also be able to classify a particular patient’s disease. Thus, this paper shines the light on the importance of multi spectral classification which means the collection of several monochrome images of the same scene. It can be proved to be an important process in the healthcare areas to know if a patient is suffering from a specific disease or not. The convolutional layer followed by the pooling layer is used for the feature extraction process and for the classification process; fully connected layers followed by the regression layer are used.</p></abstract>

According to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in healthcare domain. As there are lots of subdomains which come under the category of healthcare domain, the proposed paper concentrates on one such domain, that is breast cancer and pneumonia. Today, just classifying the diseases is not enough. The system should also be able to classify a particular patient’s disease. Thus, this paper shines the light on the importance of multi spectral classification which means the collection of several monochrome images of the same scene. It can be proved to be an important process in the healthcare areas to know if a patient is suffering from a specific disease or not. The convolutional layer followed by the pooling layer is used for the feature extraction process and for the classification process; fully connected layers followed by the regression layer are used.

Multi spectral classification and recognition of breast cancer and pneumonia

Polish Journal of Medical Physics and Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Multi spectral classification and recognition of breast cancer and pneumonia"}

According to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in...

Multi spectral classification and recognition of breast cancer and pneumonia

Data publikacji: 03 kwi 2020

Zakres stron: 1 - 9

Otrzymano: 28 cze 2019

Przyjęty: 15 paź 2019

DOI: https://doi.org/10.2478/pjmpe-2020-0001

Słowa kluczowe
convolutional layer, pooling layer, multi spectral classification, breast cancer, pneumonia

© 2020 Aditya Kakde et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Multi spectral classification and recognition of breast cancer and pneumonia

Data publikacji: 03 kwi 2020

Zakres stron: 1 - 9

Otrzymano: 28 cze 2019

Przyjęty: 15 paź 2019

DOI: https://doi.org/10.2478/pjmpe-2020-0001

Słowa kluczoweconvolutional layer, pooling layer, multi spectral classification, breast cancer, pneumonia

© 2020 Aditya Kakde et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
convolutional layer, pooling layer, multi spectral classification, breast cancer, pneumonia