Prognosis and Diagnosis of Breast Cancer Using Interactive Dashboard Through Big Data Analytics

Transcription

1 Prognoss and Dagnoss of Breast Cancer Usng Interactve Dashboard Through Bg Data Analytcs Gomath N, and Sandhya P 2 * Department of Computer Scence and Engneerng, Veltech Dr. RR & Dr. SR Unversty, Avad, Chenna, Inda 2 Department of Scence and Humantes, Veltech Dr. RR & Dr. SR Unversty Unversty, Chenna, Inda * Correspondng author: Sandhya P, Assstant Professor, Department of Scence and Humantes, Veltech Dr. RR & Dr. SR Unversty Unversty, Chenna , Inda, Tel: ; E-mal: sandy.sp22@gmal.com Receved: January 7, 206; Accepted: February 27, 207; Publshed: March 0, 207 Abstract Bacground: Cancer s a lfe threatenng dsease of present scenaro among whch breast cancer s the second hghly mortal dsease n women. There are several stages of cancer and an early detecton of breast cancer can reduce the mortalty rate. The prmary detecton of breast cancer s mammography due to the naed eye predcton of the dsease by radologst, they suggest for the next level of dagnoss le MRI, PET or bopsy. These tests are tme consumng and not cost effectve. In ths wor, we am for an nteractve dashboard methodology to determne the presence or absence of a dstnct mass called tumor n mammographc mage. Here we also tend to confrm the mass to be bengn or malgnant by analyzng the shape of the mass usng mage processng technques. We also predct a possblty to determne the stages of breast cancer usng bg data and cloud as a next level to the computer aded method of cancer detecton. Methodology: In ths paper, we also use Bacpropagaton and Support vector machne system (SVM) for analyss of bengn or malgnant cancer, we can also predct the stage of cancer usng mammographc mages. Concluson: The results performed n nntrantool determne the performance rate, tranng state, regresson and error hstogram of the test mage. And the cloud and bg data analyss dea suggest to a next level of home level cancer stage predcton. Keywords: Mcrocalcfcatons; PET scan; nntrantool; ANN; SVM Introducton Breast cancer s the most commonly dagnosed cancer among women n Unted States. Breast cancer based on the stages the rs factor of the dsease also ncreases. The cancer death rate s expected to reduce f the dsease was to be dagnosed earler. A perodcal mammogram for women s always recommended to reduce the rs of breast cancer especally to women wth a famly hstory of cancer []. WHO s specal agency Internatonal agency of research on cancer evaluates the expry rate of female death by breast cancer to be per year and t consecutvely ncreasng every year [2]. The dfferent stages of breast cancer are classfed to dfferent stages as the tumor sze n breast and ts nvason that has occurred n the auxlary lymph nodes and other organs of human body [3]. The followng are the stages of breast cancer explanng the characterstcs of cancer cell. Ctaton: Gomath N, Sandhya P. Prognoss and Dagnoss of Breast Cancer Usng Interactve Dashboard Through Bg Data Analytcs. Botechnol Ind J. 207;3(): Trade Scence Inc.

2 March-207 The frst step to detecton of cancer n human body s by mammography. The mammogram mages are vsually examned by radologst and report on the presence of cancer s gven as postve or negatve. The accuracy of vsual concepton of mammographc mages s less due to the optcal wearness (TABLE and FIG. ). To ncrease the accuracy of mammographc readng, we can follow several technques le computer aded desgn, neural networ, mage processng etc. [4]. In few prevous wors, they focus on ncreasng the accuracy of mammography readng, ease of dsease detecton and to assst patents n a self-confrmaton of dsease and stages of dsease nstead of a second opnon. In several cases the mammographc screenng leads to msnterpretaton of noncancerous growth and the predcton can be termed as falsepostve value for postve and false-negatve value for negatve. There are also several cases where the screenng gves a type I error.e. false-negatve error on a postve case [5]. Ths leads to the severty of undentfed cancer wthn a short perod of tme due to hgh rate of prolferaton. Sometmes even hghly dense breast gves very less nformaton on cancerous cell presence by mammography due to the lac of accuracy normally the radologst drects the patents for a next level of detecton such as MRI, PET scan and bopsy. In such cases the computer scence creates a great platform to the next level of dsease determnaton that reduces cost and tme [6,7]. The outputs of mammography wth the help of computer technologes help to predct cancer and the cancer present to be bengn or malgnant. The varous technques le Machne learnng technques, and Deep Learnng methods and computer aded desgn technologes are utlzed to detect the presence of cancer cells from the mammographc mages. The most common tool that has nspred human n modelng complex nonlnear functon s Artfcal Neural networ (ANN). Ths model recognzes the mage patterns and helps n the detecton of lumps n the mammogram mages [8,9]. TABLE. Dfferent stages of breast cancer [3]. Stage Stage 0 Is carcnoma n stu Stage Defnton Tumors that have not grown beyond ther ste of orgn and nvaded the neghbourng tssue. they nclude: -ductal carcnoma n stu -lobular carcnoma n stu Tumor sze <2 cm, metastases to other organs and tssues not avalable Stage 2a Tumor <2 cm n cross-secton wth nvolvement of the lymph node or tumor from 2 cm to 5 cm wthout nvolvement of the axllary lymph nodes Stage 2b Stage 3a Stage 3b Stage 3c Stage 4 Tumor more than 5 cm n cross-secton (the result of the axllary lymph node research s negatve for cancer cells) or tumor from 2 cm to 5cm n dameter wth nvolvement of axllary lymph nodes Also, called local spread of breast cancer: tumor more than 5 cm wth spread to axllary lymph nodes or tumor of any sze wth metastases n axllary lymph nodes, whch are ntted to each other or wth the surroundng tssues Tumor of any sze wth metastases nto the sn, chest wall or nternal lymph nodes of the mammary glands (located below the breast nsde the chest) Tumor of any sze wth a more wde spread metastases and nvolvement of more lymph nodes Defned as the presence of tumors (regardless of the szes ), spread to the parts of the body that are located far removed from the chest(bones, lungs,lver, bran or dstant lymph nodes) 2

3 March-207 The computer aded detecton s used n the feld of medcne recently to reduce the medcal expenses. Ths technque s used to determne the doubtful leson and characterze the malgnancy of the specfc leson. The detecton sde of ths technque, the CADe helps to recognze and locate the cancerous area from the nput mage whereas the CADx system helps to dagnose the dsease and classfy the cancerous locaton to be bengn or malgnant tssue [0-3]. The cancer detecton tool CADe follows a regon based or pxel based algorthm to extract the Regon of nterest (ROIs) from the mammographc mages. These methods consder the sze, color, and morphology of the tumor. When the nput feed of the sze and morphology of the tumor s fed to the algorthm helps n detectng the regon to be cancerous or normal. By sharpness and the margn dstncton n any mammography mage the computer aded desgn searches for a specfc mass that denotes greater possblty to malgnancy. From the mage after the dstncton of the mass there needs to be an extracton of the regon wth a hgher resoluton. The next step s the classfcaton of the extracted mage to be bengn or malgnant [4-7]. Mcrocalcfcatons are small calcum deposts that mght be a symbol of startng stage of cancer n the future. Technques le walvelet transforms, local area threshold are used to locate hgh spatal frequences n the mage to locate the calcfcaton [8,9]. Rght after mammography, MRI and PET bopsy s a compulsory opton for the confrmaton of grades of cancer. By usng a CADx system the mage extracted and expected to be the tumor mass can be predcted as bengn or malgnant [20]. The man am of ths wor s to use mage processng technque, neural networng to predct tumor and by usng bac propagaton and support vector machne algorthm to detect weather the extracted tumor s bengn or malgnant. Here as a next level of computer aded predcton we propose a method to determne the extracted tumor mage as stage I, II, III or IV. Ths method gves better accuracy and reduces the tme and cost consumpton compared to the tradtonal methods of cancer stage detecton. FIG.. Mammographc mages of dfferent stages of cancer. The mammographc mages of dfferent stages of cancer. (a) Stage o, where the tumor s bengn, (b) Stage IA, where the tumor s malgnant wthout nvason, (c) Stage IB, where the tumor s malgnant wth nvason, (d) Stage IIA, where more than 2cm tumor observed, (e) Stage IIB, 3

4 March-207 where 0.2 mm to 2 mm szed nvason observed, (f) Stage III, where a tumor more than 5 cm observed, (g) Stage IV, where a very large tumor wth hgh spreadng to numerous regon n breast and outsde breast. Materals and methods Proposed system We suggest n ths paper a unque method of dgtal mammogram readng. There are several computer aded and mage processng methods to determne the presence of breast cancer. In ths paper we propose a method to determne the presence of breast cancer usng Deep learnng algorthm. Here we mplement to use an ansotropc dffuson flterng algorthm to reduce the mage nose that s observed n a negatve le mammogram mage. The mammogram mage s subjected to pea sgnal to nose rato, mean squared error, and contrast to nose rato as a measure of mage qualty to ncrease the sharpness of the mage. We also use a bacpropagaton and support vector machne as an optmzaton and regresson analyss. Bg data analyss For analyzng and storng a huge amount of mammographc mages, bg data analytcs s a soluton. The lfe cycle of bg data s the prmary predcton system. The system stmulates a predctng model, analysng dsease patterns and statstcal mplements and algorthms to mprove clncal trbulaton desgn and more. The system beng the ntal stage but s the future of the whle research of some mmensely colossal data analytcs s promsng for a healther populaton wth an ncrease of lfe expectancy and reducton of health care cost (SCHEME ). SCHEME. Complete flowchart of dagnoss of breast cancer usng nteractve dashboard through bgdata analyss. Machne learnng Machne learnng based on Artfcal Neural Networs and Support Vector Machnes have predcted a better accuracy n tumor detecton and mcrocalcfcaton dfferentaton. The mass and mcrocalcfcatons are to be dfferentated to dfferentate a tumor from bengn or malgnant. So the dfferentaton s usually done by the shape and margn of the mass (FIG. 2). The system by a superfcal mode determnes the mass shape to be regular or rregular. In most cases the regular mage s bengn and the rregular beng malgnant [2-23]. The followng are the few machne learnng methods: 4

5 March-207 SVM-Based featureless approach Here the SVM utlzes 2 classfers whereas the mass retrvaton s performed by the frst SVM and the other reduces false postve. The nput datasets from the mammographc mages are used as a source to compare wth the test mage. The stored nput mages are the traned mages and t s done usng the ANN based classfers and logstc regresson. The automated CAD helps n the refnng of the test mage by usng edge based segmentaton to extract ROI. The followng fve modules (Preprocessng, Flterng process, Enhancement process, Feature extracton process, Classfcaton process) are followed to get to a concluson of bengn or malgnant tssue from the ROI mage extracted from the test mage. FIG. 2. Neural networ tranng tool. Preprocessng The pre-processng of the nput mage s performed to recognze any abnormalty when compared to a normal breast mage [24]. Step : The test mage s supermposed over a normal breast mage to restrct the further analyss to Regon of Interest (ROI). The only dfference from the normal breast mage to the test mage wll be the tumor. Ths specfc tumor regon wll be extracted and that s consdered as regon of nterest. Step 2: In our frst module, we reduce Specle nose, a nose that reduces the qualty of the mage n a form of synthetc aperture radar (SAR), actve radar, medcal ultrasound and coherence tomography mages. Step 3: The mage s saved to a JPG, DICOM, and PGM format wth an mage dmenson changed to 256*256 or 52*52 Step 4: The sze of the mage s calculated. Step 5: Specle nose reducton s acheved by convertng the mage to grey color. Flterng process A nonlnear medan flterng operaton commonly used n mage processng s a major tool to reduce nose n mage. Most mages have varatons n llumnaton and ntensty. There s also a possblty of poor contrast leadng to the dscomfort n usng the mage drectly. Due to ths major reason, we follow a process called as flterng to convert the pxel ntensty of mage to mprove the corrupted mage and also ths process mproves the contrast of the mage by removng noses and helps n revealng certan mnute characterstcs of mage. There wll be template matchng performed that detects the nown patterns of the mage to the bacground mage gven [25]. Step : In our next module, we use medan flterng n matlab 5

6 March-207 Step 2: B = medflt2 (A) Medan flterng performs a medan flterng of the matrx A n two dmensons. The output obtaned s n pxel and the result contans a medan value n a 3-by-3 neghborhood around the correspondng pxel n the nput mage. Step 3: Perona-Mal dffuson, another name for ansotropc dffuson s a technque to perform nose reducton wthout affectng the mage or removng any mportant parts from the mage content. Enhancement process To mprove the dgtal mage qualty and to adjust the mage qualty the process of enlargement s usually used. Here we can brghten an mage, remove the nose and also sharpen the mage whch helps n the easer detecton of ey features [26]. Step : To perform the process of enhancement a hstogram technque s followed. Step 2: Hstogram(X) creates a hstogram plot of X where the automatc bucetng s performed. It s performed to reduce mnor observaton errors. The man data values uses an automatc bucetn algorthm that returns bns wth a unform range that covers a specfc range of elements n X focusng the bacground shape of dstrbuton. Feature extracton process In mage processng the basc feature extracton gven an ntal set of values and develops a set of derved values that s expected to be nformatve and non-redundant [27]. Ths leads to the followng steps: Step : A large nput data to an algorthm that s needed to be performed can be transformed to a set of specfc features that s reduced to a lmted sze. Step 2: The extracted features wth the necessary nput to be used for further process as t s better than usng the complete mage of larger sze. Classfcaton process The support vector machne algorthm s used as a fnal process here to dfferentate and splt -ve and +ve. If -ve Breast cancer not confrmed s the result obtaned. If +ve Breast cancer s confrmed and we analyss what nd of breast cancer usng bac propagaton and support vector machne. Bac propagaton The mult-layered feed forward neural networ s a basc structural unt where there s a multple nput fed wth characterstc features n t that can be compared to the output mage. The multlayer nput only plays the role of passng the nput vector feedng to the networ. Now the hdden layer that has a connecton wth nput layer s also connected to the output layer wth connecton weghts. The varatons of the bac propagaton algorthm are as follows: The unque approach to mnmze the errors s by usng gradent descent [R (θ)], called bac-propagaton n ths settng [28]. The updated weghts and bases follow, T Let z = ( + x ) m and z = (z, z,,z ) 0m m 2 M. Then: 6

7 March-207 7, R 2 N K N x f y R Wth the followng dervatves: 2, R m T m z z g x f y l T m T K ml x x z g x f y 2 R These dervatves gve a (r + ) st teraton where the learnng rate s denoted as :, ) ( N r m r r m r m R, ) ( N r ml r r ml r ml R The smplfed dervatves are, K m T m S m x The and are quanttes called as errors The output layer errors K m T m m x As ther defnton satsfyng the bac-propagaton equatons:, m m R z

8 March-207 R m x ml l, The two pass algorthm s used as a forward pass and bacward pass to revew on the errors. The penalty to the error, J R where: J 2 m m ml 2 ml Algorthm In the early stage assgn the networ values (as small values) do for (every tranng example Forecast= neural networ output real = tran-output (ex) calculate error(forecast-real) at the producton unts calculate for all values from hdden layer drected to output layer calculate for all values from nput layer drected to hdden layer renew networ values untl all examples dfferentated correctly return the networ SVM algorthm: It s a supervsed learnng model wth a learnng algorthm that uses the nput data to gve a result on regresson analyss. A non-probablstc bnary lnear classfcaton called ernel trc s used to map ther nputs nto a hgh dmensonal feature spaces. It s a hyperplane wth nfnte dmensonal space that s utlzed for regresson calculaton, classfcaton etc. The hyperplanes set a ernel functon x y The hyperplane vector parameter: The hyperplane feature vector: The hyperplane feature space: x x, vector n the space. These are mapped n space wth the relaton: x x cons tan t Neural networ tranng tool: The functon of nntrantool provdes hdden layers and hdden neuron based on the complexty of the tool. The followng characterstcs of functon are: Formula Data Hdden data Threshold Repettons 8

9 March-207 Start weghts Algorthm The number of hdden neurons should be determned n relaton to the needed complexty. Levenberg-Marquardt Second order tranng speed followed the quas-newton methods, n whch the Levenberg-Marquardt algorthm was desgned [29]. The tool helps n gvng the followng results Performance Tranng state Error hstogram Regresson Exstng System and Proposed System Comparson of performance-perceptron and SVM In the below chart we can very dstnctvely fnd that perceptron, prevously followed computer aded tool showed less accuracy compared to the SVM tool (FIG. 3a). Comparson of performance-bacpropagaton and SVM The comparson s done usng the two mportant tools of Artfcal Neural Networs. The performance rates of the tools are shown n the form of chart. The chart explans the performance of Bacpropagaton and SVM to be effectve and accurate wth a hgh-performance rate (FIG. 3b). FIG. 3(a) FIG. 3(b) FIG. 3. Exstng and proposed system comparson Exstng and proposed system comparson (Fg 3a) Comparson of performance of perceptron and SVM (Fg 3b) Comparson of performance of Bacpropagaton and SVM. 9

10 March-207 Results Ths paper, establshes a unque method of hgh accuracy to predct a computer aded breast cancer analyss. The usage of Artfcal Neural Networ wth a Support Vector Machne and Bacpropagaton algorthm to classfy the mass of tumor as bengn or malgnant has been dscovered to be a very effcent method wth hgh precson. The prevously used tool perceptron was shown wth a less performance rate when compared to the bac propagaton and SVM tool. The presence f breast cancer and the classfcaton of breast cancer wth hgh accuracy can be performed and confrmed wth neural networng tools. In ths paper, we also propose a new feld of research to whch the ROI extracted can be compared to a seres of nput mage to determne the shape and sze of the mage. Once the sze of the mage s determned we can predct the stages of breast cancer an early determnaton of breast cancer s curable. Ths same protocol extendng to the next level of stage determnaton of breast cancer can also be extended and proved usng the same method. The test mage fed as nput (FIG. 4) to the system s compared to the healthy breast mage. The test mage we have fed has a presence of tumor. Ths test mage s reszed and fltered to determne the mass regon of the breast (FIG. 5). When a supermposng of the mage over the normal mage s done only the mass expected to be the tumor s dstngushed. In ths test mage the mass s fgured fltered to ncrease the hazness n the mage and dstngushed as a regon of nterest (ROI) (FIG. 6). The fltered mage ncreases the accuracy to determne the ROI. The ROI mage s extracted separately eroson and segmentaton of the mage s performed to remove the nose n the mage (FIG. 7). The mage where the nose s removed undergoes an analyss to determne the calcfcaton and shape of the mage (FIG. 8). Based on the ROI the mass s separated and detected to gve the end result as presence of Breast Cancer (FIG. 9). For the classfcaton of breast cancer to be bengn or malgnant a major analyss of the mass s to be observed. If the mass extracted s of a specfc shape probably rounded, then the cancer has not started ts prolferaton to the neghborng regon. So, f the mass observed s round or wth a specfc shape then the cancer expected can be classfed as bengn. If there s any rregular shape observed n the extracted mass t can be concluded as the tumor has started spreadng to the neghborng regons leadng to an rregular mass. Ths rregular mass observed can be proposed as malgnant cancer. The tumor determned output fed to the neural networ tranng tool (FIG. 0) performances several tass to confrm the accuracy of the mage extracton and result predcton. The tranng tool gves a performance result that shows a mean squared error chart as a comparson wth tranng set and the test nput (FIG. ). The tool also gves an error hstogram result, regresson chart and tranng state of the nput mage (FIG. 2-4). These are tools for a confrmaton of accuracy determnaton n the process performed. Dscusson and Concluson Ths paper, establshes a unque method of hgh accuracy to predct a computer aded breast cancer analyss. The usage of Artfcal Neural Networ wth a Support Vector Machne and Bacpropagaton algorthm to classfy the mass of tumor as bengn or malgnant has been dscovered to be a very effcent method wth hgh precson. The prevously used tool perceptron was shown wth a less performance rate when compared to the bac propagaton and SVM tool. The presence of breast cancer and the classfcaton of breast cancer wth hgh accuracy can be performed and confrmed wth neural networng tools. 0

11 March-207 FIG. 4. Process Output. Test mage saved s opened to follow the serous of process to determne the tumour. FIG. 5. Process output 2. Supermposton of test mage wth data sets, reszng and flterng of the mammographc mage.

12 March-207 FIG. 6. Process output 3. The Regon of Interest (ROI) extracton process. FIG. 7. Process output 4. The eroson and segmentaton process. 2

13 March-207 FIG. 8. Process output 5. Enhancement and calcfcaton cancer regons. FIG. 9. Process output 6. Presence of Breast cancer confrmaton. FIG. 0. Process output 7-Neural Networ Tranng (nntrantool) Neural networ tranng tool-fed wth the output result. 3

14 March-207 FIG.. Neural networ tranng tool-performance chart. Performance chart-the test mage showng the 0 Mean Squared Error performance rate when compared to the valdaton and tran data set values. FIG. 2. Neural Networ Tranng state gradent graph. 4

15 March-207 FIG. 3. Neural networ tranng error hstogram. FIG. 4. Neural networ regresson graph. 5

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