Earlier Detection of Oral Cancer Using Photo Plethysmography

 INTRODUCTION

The aim of this blog is to detect oral cancer by using photoplethysmography. The unit of Photoplethysmography detects the cancerous tissue in the region of interest by the difference in the blood volume. The fuzzy technique is then used to identify the cancer stage, according to clinical TNM norms. Picture plethysmography is based on the ophthalmic properties on a chosen skin part. Nonvisible IR light is released through the skin for this reason. Light is consumed more or less, subject to the amount of body fluid in the eyes. The backscattered light matches, therefore, to the variance of the capacity of the blood. Variations in blood volume can then be measured by calculating the replicated light and using tissue and blood ophthalmic properties.

Figure 1 shows the oral cancer detector block diagram. It consists of three main parts.

1) Photoplethysmography 2) preprocessing unit and 3) Fuzzy unit.

Fig.1 “Block Diagram of Fuzzy Based Photo Plethysmography unit for Detection of Oral Cancer”

Cancerous tissues contribute to increased vascularization resulting in increased blood flow. The flow of blood here is measured using reflected light. The peripheral body reflects the light produced by the source, and this is measured by the photodiode. This replicated signal is handled by means of two 30 Hz LPF and to obtain the gain the signal will be amplified. This is will be converted to digital form, kept and exhibited by a segment of the computer. Thus portions affected by cancer show more light suggesting increased vascularization.

This technique involves the positioning of a light source over a vascular bed and the light detector.

The light values differ in the photodetector is due to the blood flow in the vascular bed. Nonstop monitoring of pulses of the blood pressure and the amount of blood is obtained. GaAs LED, which generates a fine band source with a spectral release at a wavelength of 940 nm, can be used for the light source to make a less bulky device.

Fig.2 “Characteristics of Photo Plethysmography”

The photoconductive cells are very large and it is very less sensitive when the change in blood flow, therefore to increase the sensitivity of the receiver we use a-Si phototransistor. The frequencies below 120 Hz will be eliminated with the help of filters because we have other sources of light also. These devices are provided for in order to prevent the daylight proof enclosures.

The sensor output reflects a great transmittance value, which is modified by very minor variations due to blood throbs. Frequencies above 0.05Hz are accepted over an HPF to remove the broad baseline value. This signal is amplified, and when there is movement in photoplethysmograph compared to tissue, there will be a significant shift in the baseline value.

The formula to calibrate the blood volume from output voltage is

“   ΔV= (ρL²/Z_O ²) ΔZ                                (1)

Where,

ρ        – Exact resistance (150-ohm cm)

L       – Distance between source and detector (5 mm)

Z_O     – Basal impedance (230 ohm)

ΔZ     – (V_O/I_O)

V_O     -Output Voltage

I_O      –  Constant Current.”

A.     “Cancer Classification”

The tumorous patients are clustered using the TNM clinical standard. Based on the size of the tumor  T is classified

“T1-Less than 2 cm

T2- 2 to 5 cm depth

T3- 5 to 7 cm depth

T4- greater than 7 cm

The nodes are classified as below

N0- no node

N1- movable secondary nodes, which are different from secondaries

N2- stationary nodes”

B.     “Solution Constraints”

Fuzzy a sort of logical (or) a mathematical setup by classifying (or) qualified sentence, aids us categorize the cancer level. Since fuzzy logic is not numbered, normal terms are used as minor, moderate, extreme, and very serious, which is easily understandable.

The analyses we got from the part of “photoplethysmography” are to be classified so that they do not have any falsification. Here we use Fuzzy logic to describe the values at various stages.

The fuzzy set theory offers us with hypothetical tools to deal with natural language concepts that allow us to denote linguistic ideas. The Fuzzy logic can be used as follows,

Major input and output variables.

1. Classifying cancer using “various FAM (Fuzzy associative Memories) rules”.

C.     “Fuzzy Interference Rule”

The input to the fuzzy toolbox is the variation of blood volume and it is measured using the area of interest, and the output is defuzzified value. From this, we can establish the clinical standard for patient status as per TNM.

II.     Fuzzy MEMBERSHIP FUNCTION EDITOR

                                               The editor manages multiple input and output variables for the system’s high-level problems ( i.e.). The toolbox Fuzzy logic does not bound the number of inputs. The FIS corrector presents overall knowledge regarding a whimsical inference method. The shapes of all membership functions are described using the membership function for output variables related to increasing input variables and membership function.

Fig.3 Membership Function Editor

 Result and discussion

     Oral cancer is India’s most common disease, responsible for 50-70 percent of overall deaths from cancer. The high amount of patients among males can be due to the high occurrence of tobacco use behavior While oral cancer occurs at sites that are available for medical test and can be modified to be diagnosed using present diagnostic methods, the root of the trouble is that most of the patients came late to the health care center as obvious from the result of this study. Comparison is made between the classification effectiveness of both the medical standard and neural network. The neural network adheres closely to medical classifications. So this method can also be used as a premature method for cancer stage classification.

The application of neural networks in the medical field gives clinical medicine tremendous promise. This makes the diagnostic process easier, quicker, and without mistakes. The new, novel approach is a benefit to working oncologist. This methodology is particularly beneficial because it minimizes viewer bias, making it easier to compare outcomes across persons and various approaches. Teaching a network can take more time, but they produce consistent outcomes once they have been educated.

     Cancer can be diagnosed from the fraction variation of blood volume determined by the position of interest with the medical norm TNM based on different findings found on subjects. We note that the usual level of blood volume for the” Madurai area is less than that of Chennai and Coimbatore”

Conclusion

     In this study, most cases of alveolus carcinoma might be associated with the habit of chewing tobacco. Smokeless spit tobacco contains more than 1000 chemicals; some are directly related to cancer-causing. Tobacco use is a fixed risk factor for oral cancer development. It is linked to the dose and behaviors consuming tobacco throughout, as found in this study. Therefore, based on the results of this report, community health education about tobacco use risks in terms of oral cancer growth is recommended; full cancer longevity in earlier stages and awareness about oral cancer risk is recommended.