Multi spectral classification and recognition of breast cancer and pneumonia

• Autorzy: Kakde Aditya , Arora Nitin , Sharma Durgansh , Sharma Subhash Chander

Identyfikatory

DOI

10.2478/pjmpe-2020-0001

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

convolutional layer; pooling layer; multispectral classification; breast cancer; pneumonia

• Wydawca: Polskie Towarzystwo Fizyki Medycznej ; De Gruyter
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2020
• Tom: Vol. 26, Iss. 1
• Strony: 1--9
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Kakde Aditya

• School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India

autor

Arora Nitin

• Electronics & Computer Discipline, Indian Institute of Technology, Roorkee, India

autor

Sharma Durgansh

• School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India

autor

Sharma Subhash Chander

• Electronics & Computer Discipline, Indian Institute of Technology, Roorkee, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).