Backpropagation Neural Network for Analysis and Classification of Fluorescence Spectroscopy of Squamous Cell Carcinoma in Animal Model

Author(s)

Nogueira, Joao Marcelo ; Garcia, Marlon Rodrigues ; Requena, Michelle Barreto ; Moriyama, Lilian Tan ; Pratavieira, Sebastiao ; Magalhaes, Daniel Varela ; IEEE

• Document type: Journal article
• Source: 2021 SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE (SBFOTON IOPC); v. N/A, p. 4-pg., 2021-01-01.

Abstract

The present study aims to evaluate the performance of a backpropagation neural network (BPNN) using the principal component analysis (PCA) of fluorescence spectra for discrimination between normal skin and skin tumor on mice. The fluorescence spectra were acquired from nude mice with induced squamous cell carcinoma (SCC). The artificial neural network (ANN) used in this study is a classical multiplayer feed-forward type with a back-propagation algorithm. The classification results show this technique as promising for healthy and unhealthy tissue classification. During the validation, the network classified 100% of the training set spectra and 90% of the test set. (AU)

• FAPESP's process: 13/07276-1 - CEPOF - Optics and Photonic Research Center
• Grantee: Vanderlei Salvador Bagnato
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 14/50857-8 - National Institute in Basic Optics and Applied to Life Sciences
• Grantee: Vanderlei Salvador Bagnato
• Support Opportunities: Research Projects - Thematic Grants