cancer project uma,backialakshmi

7/31/2019 Cancer Project Uma,Backialakshmi

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Guide

Mr.M.Venkatesh SaravanaKumar MCA.,M.Phil.,

Done by

G.Backyalakshmi

S.Uma Devi


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ABSRTACT

Cancer classification is the critical basis for

patient-tailored therapy. Conventional histological analysis

tends to be unreliable because different tumors may havesimilar appearance . Various machine learning methods can be

employed to classify cancer tissue sample based on microarray

data. In this project , a methodology for classifying cancer

based on the principles of minimal rough fringe has been

proposed. It is capable of generating accurate &interpretable

rules composed with some other machine learning methods.

Hence ,it is a feasible way of classifying cancer tissues inbiomedical application.


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MODULE DESCRIPTION

Input Module

Cancer Cell Report.

Pre Processing moduleStoring the Cancer Cell samples to the system.

Classification module

Compare the report with Database.

Segmentation module

Finding Cancer cell classification.

Cancer Type Detection Module

Name of the Cancer.

In this module, the key in cancer cell have been analyzed and its type

will be displayed.


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INPUT MODULE:

This module is deals with getting the blood report or

cancer affected patient blood sample.

PRE PROCESSING MODULE:

This module stores the blood samples for the comparingpurpose. Blood sample may be in the form of binary number.

CLASSIFICATION MODULE:

This module compares the input Cancer blood sample

with the stored Cancer blood sample. This module also helps us

to classify the type of the Cancer.


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SEGMENTATION MODULE:

Segmentation is for finding edges of the blood cell,

finding the blood cell count from the blood sample.

CANCER TYPE DETECTION MODULE:

This module analysis the report from segmentation andidentifies the cancer type.


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SYSTEM REQUIREMENTS

HARDWARE REQUIREMENTS

Processor : Pentium IV

Hard Disk : 40 GBRam : 256 MB

Monitor : 15VGA Color

Mouse : Ball / OpticalKeyboard : 102 Keys


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SOFTWARE REQUIREMENTS

Operating System : Windows XP professional

Framework : Microsoft Visual Studio .Net 2005

Language : Visual C#.NetBack End : SQL Server 2000


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Level 0:

Input

System

Cancertype

report


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Level 1:

Analyze thereport Sampleclassification Gray scaledsample

Edgedetection

Input

System

Preproces

sing

Cancer

typereport


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Level 1.1

Classification

Traditionalclassifier

Class wiseclassifier

Database

Preprocessing

System

Classwise countTraditional Count


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Level 2

segmentation

Segment the stages of cells

Finding the count of nucleus

Cancer type detection

Report

Database

Preprocessing


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Table

Sid Int Not nullMyloblast Int Null

Promylocute Int Null

Myelocyte Int Null

Metamylocate Int Null

Band Int Null

Pmnny Int Null

Disease Varchar(50) Null


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USECASE DIAGRAM


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Preprocessing


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ClassificationPreprocessing


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Preprocessing SegmentationClassification


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Screen Layout :


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Collecting Cancer specific Data.

Placing the Cancer image in the micro array.


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Future Enhancement Similarities among various cancer types can be

identified.

The growth rate of specific cancer tumor can bemeasured.


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In the paper, a new approach is introduced for classifying

different types of cancers based on the rough sets theory. By

dynamically constructing implicit hyper cuboids, the approach

selects potential functional genes for inducing classifiers.

Experimental results show that the induced classifiers are

capable of classifying cancers with high accuracy , while only asmall number of genes are involved.

The results suggest that the proposed method is a feasible

way of classifying different cancer types in applications.

Future efforts can be devoted to the enhancement of themethod for manipulating noises in data by employing variable

precision rough sets.


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cancer project uma,backialakshmi

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