Multi spectral classification and recognition of breast cancer and pneumonia

Author:

Kakde Aditya1,Arora Nitin2,Sharma Durgansh1,Sharma Subhash Chander2

Affiliation:

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

2. Electronics & Computer Discipline, Indian Institute of Technology , Roorkee , India

Abstract

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.

Publisher

Walter de Gruyter GmbH

Reference28 articles.

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2. [2] Micalizzi DS, Maheswaran S. On the trail of invasive cells in breast cancer. Nature. 2018;554:308-309.

3. [3] Mayo Clinic Patient Care and Health Information. Breast Cancer. https://www.mayoclinic.org/diseases-conditions/breast-cancer/symptoms-causes/syc-20352470

4. [4] LeCun Y, Bengio Y. Convolutional Networks for Images, Speech and Time-Series. in: The handbook of brain theory and neural networks. MIT Press Cambridge; 1998.

5. [5] Kakde A, Arora A, Sharma D. Novel Approach towards Optimal Classification using Multilayer Perceptron. Int J Res Eng IC Soc Sci. 2018;8(10):29-38.

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