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Artificial neural networks for processing fluorescence spectroscopy data in skin cancer

diagnostics

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2013 Phys. Scr. 2013 014057

(http://iopscience.iop.org/1402-4896/2013/T157/014057)

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IOP PUBLISHING PHYSICASCRIPTA

Phys. Scr.T157(2013) 014057 (4pp) doi:10.1088/0031-8949/2013/T157/014057

Artificial neural networks for processing

fluorescence spectroscopy data in skincancer diagnostics

L Lenhardt, I Zekovic, T Dramicanin and M D Dramicanin

Institute for Nuclear Sciences Vinca, University of Belgrade, PO Box 522, 11001 Belgrade, Serbia

E-mail:[email protected]

Received 25 August 2012

Accepted for publication 14 January 2013

Published 15 November 2013

Online atstacks.iop.org/PhysScr/T157/014057

Abstract

Over the years various optical spectroscopic techniques have been widely used as diagnostic

tools in the discrimination of many types of malignant diseases. Recently, synchronous

fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer

diagnostics. The SFS method involves simultaneous scanning of both emission and excitation

wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or

frequencies (constant-energy mode) between them constant. This method is fast, relatively

inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal

skin, nevus and melanoma samples were used as input for training of artificial neural

networks. Two different types of artificial neural networks were trained, the self-organizing

map and the feed-forward neural network. Histopathology results of investigated skin sampleswere used as the gold standard for network output. Based on the obtained classification

success rate of neural networks, we concluded that both networks provided high sensitivity

with classification errors between 2 and 4%.

PACS numbers: 87.19.xj, 87.64.K, 87.64.kv

1. Introduction

Skin cancer is one of the most common malignancies

worldwide. Late detection delivers high mortality rates, but

if diagnosed in the early stages it is one of the most treatableforms of cancer. Taking this problem into account, developingnew methods for cancer diagnosis is of crucial significance.In the last few decades, in the field of cancer diagnostics,fluorescence spectroscopy has proven to be a very promising

technique (Alfano et al 1987,Sterenborg et al 1994,Palmeret al2003).

Human tissues are a complex mixture of differentmolecules and some of these molecules, called endogenousfluorophores, have the ability to absorb and emit light ofdifferent wavelengths. The concentration and distribution ofvarious fluorophores in the skin such as nicotinamide adenine

dinucleotide (NADH), flavine adenine dinucleotide (FAD),

keratin, collagen, elastin, amino acids, lipids and porphyrinsare the fundamentals for discrimination between cancer andnormal tissue by fluorescence spectroscopy. In recent years ithas been shown that synchronous fluorescence spectroscopy

(SFS) can be successfully used in cancer detection (Vo-Dinh2000, Vengadesan et al 2002, Dramicanin et al 2005,2006, 2011). This method involves simultaneous scanningof both emission and excitation wavelengths while keeping

the interval of wavelengths (constant-wavelength mode) orfrequencies (constant-energy mode) between them constant.The advantage of a synchronous spectrum over ordinaryemission spectra is that it often has more features andprovides more information. The obtained data are usuallyhighly correlated with subtle differences between abnormaland normal tissues, and for this purpose artificial neuralnetworks (ANN) can be very useful for building a diagnosticmodel. ANN is an adaptive system that modifies structurebased on input findings to generate robust output. The benefitof this method is its consistency and objectivity due tolack of human fatigue and bias. In this paper, two differenttypes of ANN were trained, the self-organizing map (SOM)

and the feed-forward neural network. SOM is one of themost popular neural networks that convert high-dimensionalnonlinear statistical relationships into simple geometricrelationships in an unsupervised way (Kohonen 2001).

0031-8949/13/014057+04$33.00 1 2013 The Royal Swedish Academy of Sciences Printed in the UK
http://dx.doi.org/10.1088/0031-8949/2013/T157/014057mailto:[email protected]://stacks.iop.org/PhysScr/T157/014057http://stacks.iop.org/PhysScr/T157/014057mailto:[email protected]://dx.doi.org/10.1088/0031-8949/2013/T157/014057

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Phys. Scr.T157(2013) 014057 L Lenhardtet al

Figure 1. Total synchronous fluorescence spectra of different skin sample types: (a) melanoma, (b) nevus and (c) normal skin.

Unlike SOM, the feed-forward neural network is trained in

a supervised way with prior knowledge of a samples group

membership by data entering at the inputs passing through

layers of neurons until it arrives at the outputs without any

feedback information between layers (Jiang et al 2003).

ANN are often trained to be further used as classification

models (Rhee et al 2005, Dramicanin et al 2009). The aim

of this work was to investigate the possibility of building a

skin cancer diagnostic method by training ANN using total

fluorescence spectra of different types of skin lesions.

2. Materials and methods

Synchronous fluorescence spectra of 50 skin samples (12

melanoma, 18 nevus and 20 normal skin samples) were

measured ex vivo using a fluorescence spectrophotometer

(Perkin Elmer LS45) in constant-wavelength mode. The

spectra were collected at a scan rate of 200 nm min1 in the

excitation range from 330 to 550 nm and the wavelength

offset range from 30 to 120 nm and automatically

normalized to excitation by the instrument. Samples were

obtained from human patients soon after surgical resection,

histopathologically identified and then measured by the

instrument at room temperature. In order to reduce the

dimensionality of obtained spectra, principal component

analysis (PCA) was applied. For training, calculated PCA

components were introduced in SOM and the feed-forward

neural network. To assess classification error, they were tested

with new samples unknown to both ANN.

3. Results and discussion

Figure 1 presents total synchronous fluorescence spectra in

the form of contour diagrams of different skin samples:

normal skin, nevus and melanoma. Emission patterns

reflect the specificity of endogenous fluorophores and their

microenvironments in skin. It can be clearly seen that the main

differences in the fluorescence of skin lesions are observable

in several spectral regions. The first region spans the excitation

wavelength from 330 to 400 nm and the synchronous interval

from 30 to 55 nm. Structural proteins of the extracellular

matrix, collagen and elastin have maximum excitation in this

region (Ramanujam 2000). The second and third spectralregions cover the excitation wavelength interval from 430 to

480 nm, 350 to 400nm and the synchronous interval from

30 to 50 nm, 65 to 90 nm, respectively. Fluorescence of

Figure 2. Synchronous fluorescence spectra taken at = 70nmof melanoma (full line), normal skin (dash line) and nevus (dot line)samples.

these regions originates from several fluorophores like the

co-enzymes NADH and FAD.A skin cancer diagnostic method based on measurement

of SFS and a classification model built using two different

ANN was developed in several steps. First reduction of

dimensionality was necessary due to the large size of gathered

SFS. For that purpose PCA was applied on SFS data

and a five-component PCA model was acquired. PCA is

an unsupervised statistical method used to distinguish and

identify patterns in data and express them in such a manner

as to point out their similarities and differences. This method

transforms a number of possibly correlated variables into

a smaller number of uncorrelated variables called principal

components, which account for most of the variance in theobserved variables. This allowed us to explore our data and

to determine for which wavelength offset value () best

separation between groups was achieved. After examining the

results of PCA, it was concluded that the best differentiation

between classes is for = 70 nm, figure2.In figure3 one

can see an obvious distinction between classes. Taking this

into consideration, 25 samples PCA scores for = 70nm

were used to train SOM and the feed-forward neural network.

In order to enlarge the number of samples for ANN training,

we created a normally distributed set of data based on the

mean value and standard deviation of 25 samples used for

training. With a new data set of 2000 samples we trained both

ANN.SOM learns to group data based on similarity and

topology, and as a result it assigns the same indices to each

class. It is used for reduction of dimensionality and clustering

2

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Figure 3. PCA scores obtained from SFS data taken at = 70nmof melanoma, normal skin and nevus samples.

Figure 4. Schematic representation of the SOM architecture.

data. We introduced a 2000 5 matrix (2000 samples and 5

parameters describing every sample) to SOM with 3 neurons

(size 1 3), figure4,and started training with 150 iterations.

Through all this, SOM did not have any information about the

samples group membership (unsupervised learning). When

trained, SOM was tested with 25 new samples unknown to

the network and the classification error was acquired. This

process of SOM training and testing was repeated several

times, every time using a different combination of data for

training and testing. Doing this we obtained a more accurate

efficiency overview of this method.

Feed-forward networks, figure5,contain series of layers.

The first layer gets network input data, and each subsequent

layer is connected to the previous layer while the final layer

gives network output. They are usually used for input to output

mapping. We used the feed-forward network with one hidden

layer to build a classification model by giving it as input the

2000 5 matrix, the same matrix used for SOM training, and

the matrix with information of class membership of all 2000

samples (supervised learning). The process of testing was the

same as for SOM; several models were built and tested using

different combinations of data, and the mean classification

error for all models was calculated.

Based on the obtained classification errors of neuralnetworks, we found that both networks provide high

sensitivity with classification errors between 2 and 4%. For

different input data the SOM classification error was between

Figure 5. Schematic representation of the feed-forward networkarchitecture.

2 and 3%, while for the feed-forward network it was between

3 and 4%.

4. Conclusion

In this work, we used statistical analysis and ANN to classify

data of synchronous fluorescence spectra with the aim of

developing a diagnostic method for skin cancer diagnosis. The

fluorescence spectra of three different types of skin lesions,

melanoma, nevus and normal skin, revealed differences

between them due to differences in concentration of various

fluorophores and their microenvironments. The application of

PCA provided data with enlarged variances between sample

groups. It is shown that both SOM and feed-forward neural

networks gave promising results with a 9698% success tissueclassification rate. Moreover, the presented method is fast,

sensitive, inexpensive and non-destructive. Based on these

findings, we can conclude that SFS combined with neural

network-based classification has good potential in melanoma

diagnostics.

Acknowledgment

This work was supported by the Serbian Ministry of Science

and Technological Development (project numbers 173049 and

45020).

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