HUMAN DISEASE DETECTION ARCHITECTURE USING DIGITAL IMAGE PROCESSING AND ARTIFICIAL NEURAL

NETWORKSGUIDED BY: M.JENATH

STUDENTS: G.R.PRIYADHARSHINIV.SUSITHRA

R.NAFSINM.THARAGESHWARI

Abstract: With the increase in probability of diseases due to environment and gene misspellings there is always a preventive notion among people. Disease prediction has been difficult because of change in DNA over time. A novel architecture is proposed to find the probability of diseases which affect the humans due to gene misspellings and change in DNA over time. In the proposed architecture DNA microarray technology is used to extract genes and using digital image processing the image of genes are characterized .The artificial neural network calculates the mutated value and helps to find the probability of disease to be affected .

INTRODUCTION :

Bio-informatics is a rapidly growing area of computer science, which melds both biological data and computer calculations. It is essential to use the genomic information in understanding human diseases and in the

identification of new molecular target for drug discovery. Each of the trillions of cells in the human body contains 46 chromosomes packed tightly into the region called nucleus. Half of the chromosomes in the nucleus come from mother and half from father. Each chromosome is a long, tightly coiled molecule called DNA [1,2,4,5] or deoxyribonucleic acid. DNA is made up of chemical building blocks like A, C, T and G. The entire length of a DNA strand consists of the above four blocks in different combinations. The entire DNA in all the chromosomes makes up the human genome. The DNA in the genome [4] is organized into units called genes. There may be as many as 30,000 genes in the genome; they are the instruction manual for making all the proteins in the body. These proteins are the physical stuff that makes up our hair, skin, heart and blood. They also control chemical reactions that regulate blood sugar and heart rate, and control how food or medicine is metabolized in the body. The way the genes “spelled” makes the difference. Misspelling in a gene can cause disease. From the normal sequence of the human genome, researchers can compare the DNA sequence [2,4] of normal and diseased

people. If there are differences in the spelling of certain genes between the two groups, it’s possible that the disease is caused by misspelling in that gene. Scientist has identified about 6000 diseases, such as Huntington disease and cystic fibrosis that are directly caused by misspellings or physical problems in single genes. But the genetic contribution to common conditions – such as diabetes and heart disease – is a part of a larger puzzle that could include diet, lifestyle, environment, and even other genes. For many of these common conditions, genetic misspellings probably makes a small contribution to disease relative to other factors, or work in concert with them to cause illness. New biological reports that are published recently says that the DNA expression changes over time.

PROBLEM DEFINITION :

A Novel prototype architecture is designed, for the disease prediction for all age groups including infants based on the combination of parent DNA using Artificial Neural Networks (ANN), which is depicted in fig.2. The proposed architecture integrates Microarray Technology, Digital Image Processing, and Disease probing and Artificial Neural Networks.

A. DNA Microarray Data Analysis DNA microarray is used to measure the expression levels of large numbers of genes. The blood or tissue samples from Bio- informatics wet lab are collected and processed through Microarray analysis. The output of this analysis will be a gene expression image,

from the DNA sequence of the given parent pair that are stored in a database in a file format .TIFF or .JPEG.

B. Gene Extraction The gene expression is the most fundamental level in which the genotype gives rise to the phenotype. Genotype contains all the hereditary information of an individual, even if genes are not expressed. DNA, susceptible to the diseases are its examples. Phenotype is an observable trait that contains expressed genes. Hair color and weight are the notable examples of phenotype. So, with the gene expression image obtained from the Microarray Analysis, the highlighted mutated genes are characterized.

C. Image Analysis of Characterized Gene The morphological adaptive image enhancement and alignment methods are applied to the extracted images. These methods are adaptive to the local characteristics of the image such as noise, background signal, or presence of edges. The waveform analysis is used to determine the location of each band that represents one nucleotide in the sequence from which the image array of the mutated part can be extracted.

D. Disease Probing As the relationship between genetic mutations and the predisposition to certain diseases are known; it becomes more important to screen for single nucleotide polymorphisms (SNP) in entire populations, using low cost and fast methods. A draw back in this technology is the enormous time consumption in the design of probes. To overcome this computational solution is implemented to automate the process of probe design for the

combination of the mutated image array in high accuracy.

E. Artificial Neural Network System The array obtained from the disease probing algorithm is analyzed and input its mutated values to previously trained network, depicted in fig.1. With the help of disease prediction algorithm, the probability of disease can be listed to infants and the comparison results will be made with support vector machines. In ANN normally only one hidden layer is used. In order to find the prediction of disease for all age group people another hidden layer is included to provide weighted values for the

environments, food habits and stress levels in the people.

Fig 1:

CONCLUSION:

The novel architecture for predicting diseases for humans of all age groups caused due to misspelling of genes and change in DNA over time. The architecture is implemented in MATLAB. The proposed system integrates Digital Image Processing, DNA microarray and Artificial Neural Networks. The expression of genes is obtained and processed to find the mutations. Further change in DNA and effect of surroundings, stress levels and food habits of the people which leads to many diseases like hypertension, diabetes are also found using this architecture.

REFERENCES

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Fig 2: Proposed architecture for detecting the probability of diseases

Micro Array

Analysis

GENE EXTRACTION

GENE EXTRACTION

ANALYSIS OF DNA wrt SURROUNDINGS AND FOOD HABITS

IMAGE ANALYSIS

IMAGE ANALYSIS

DISEASE PROBING

ARTIFICIAL NEURAL

NETWORK

Male DNA / DNA /

Female DNA

Gene Expression Image of Y

Study of the Characteristics

of Extracted Gene

Image Array

Image Array of Mutated Value and Change in

DNA Array

Study of the Characteristics

of Extracted Gene

Probability of Diseases

Gene Expression Image of X

Mutated Values Input