We propose and report the implementation of a hybrid radio-over-fiber (RoF)/ free-space optics (FSO) system employing a monolithically integrated multi-wavelength transmitter. Matheus Sêda (INATEL) Eduardo Sala (INATEL) Nicola Andriolli (National Research Council of Italy) Danilo Spadoti (UNIFEI) Juliano Oliveira (Idea Electronic Systems) Giampiero Contestabile (Scuola Superiore Sant'Anna) Arismar Cerqueira Jr. RoF/FSO System Based on a Monolithically Integrated Multi-wavelength Transmitter Xtreme gradient boost and Linear Discriminant Analysis presented the best accuracies results, indicating to be potential models for breast cancer classification tasks. FTIR images were collect from histological sections, and six machine learning models were applied and assessed. In this work, estrogen and progesterone receptors expression were evaluated using tumors biopsies from human cell lines inoculated in mice. FTIR spectroscopy imaging may be employed as an additional technique, providing extra information to help physicians. Evaluation of hormone receptors expression plays an important role to outline treatment strategies. The breast cancer is the most incident cancer in women. Moisés Oliveira Santos (IPEN) Matheus del Valle (IPEN) Sofia dos Santos (IPEN) Emerson Bernardes (IPEN) Denise Zezell (IPEN) During the validation, the network classified 100% of the training set spectra and 90% of the test set.Įvaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images The classification results show this technique as promising for healthy and unhealthy tissue classification. The artificial neural network (ANN) used in this study is a classical multiplayer feed-forward type with a back-propagation algorithm. The fluorescence spectra were acquired from nude mice with induced squamous cell carcinoma (SCC).
FOTONICA GAME ENGINE SKIN
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. João Marcelo Nogueira (USP) Marlon Rodrigues Garcia (USP) Michelle Barreto Requena (USP) Lilian Tan Moriyama (USP) Sebastião Pratavieira (USP) Daniel V> Magalhães (USP) Backpropagation Neural Network for Analysis and Classification of Fluorescence Spectroscopy of Squamous Cell Carcinoma in Animal Model
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