# Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model

Authors: Sudan Jha; Sultan Ahmad; Anoopa Arya; Bader Alouffi; Abdullah Alharbi; Meshal Alharbi; Surender Singh
Journal: Journal of Healthcare Engineering
 (2023)

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## Abstract

The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.


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## Body

## 1. Introduction

Cancer is a disease that causes abnormal changes in the body’s tissues and cells, as well as growth that is out of control. One of the types of cancer is breast cancer. The prognosis assessment of breast cancer can help patients with breast cancer improve their chances of survival. The idea behind the segmentation is to segment out the region of interest, which gives more meaning due to which analysis is more effective and precise. In females, breast cancer is quite frequent compared to other cancers and is the most prominent reason for cancer death in the world [1]. The reason behind the cause of the disease is still a mystery, and researchers are still working on the same. Few factors learned which lead to or increased the probability of developing cancer are radiation, dense breast cells, consumption of alcohol, improper living styles, etc. The way to reduce the mortality rate caused by cancer is through early detection and examination at the initial stage of cancer. Segmentation in image processing is an essential step in image processing. In this phase of image processing, we segment out the selected region for extracting the desired information to infer the conclusion. The data fetched out using ROI will be used further for accurate feature measurements. As discussed above, breast density leads to breast cancer, and it is not easy to detect cancer in dense breasts. Mammography is one of the modalities to detect masses, especially in dense breasts; it is the best suitable technique for the same [2]. Despite a few shortcomings, mammography holds a sensitivity of approximately 90% in the detection of tumours [3].Segmentation becomes robust in noisy, blurred images, and low contrast images. Images need to be preprocessed before segmentation. Multiple techniques for segmentation to segment out masses, microcalcification, pectoral muscles, and lesions have been discussed in the paper. All these first filter the image by removing a patient’s information and other extra information. The noise and contrast of the image are also modified according to the standards to get appropriate and accurate results for distinguishing benign from malignant. Various features are studied such as shape and size of tumour, texture, intensity, and grey level histogram to figure the growth [4, 5]. Mammographic images have poor contrast and noise. The image may carry both benign and malignant tissue, and the threshold (Otsu image segmentation) technique alone may not be sufficient to distinguish between both of them.The following are the main points of the paper, based on the novelty and contributions:(i)
To conduct the segmentation of mammograms with the help of different phases such as “2D median filter, CLAHE, FCM on images, removing connected components having less thanx pixels.”(ii)
To improve the segmentation accuracy by developing the algorithm which optimized the threshold value and specificity of each threshold between the data points.(iii)
For displaying the relevant captions, calculate the best value for threshold position, sensitivity, specificity, area under curve, accuracy, and all false and true positives and negatives.(iv)
To make use of the same algorithm for hybrid segmentation of mammographic images with integration of fuzzy C-Means and CNN model for optimization, which improves the accuracy.(v)
To perform segmentation of mammograms and the readings obtained on sixteen different parameters: distance, Sensitivity, Specificity, ARoC, Accuracy, PPV, NPV, FNR, FPR, FDR, FOR, F1 Score, MCC, BM, and MK.


## 2. Literature Review

Researchers have done fabulous work in the field of cancer and have learned that if the disease is detected in the early stages, then the mortality rate can be reduced much and the ratio can be improved. The best modality for early detection is mammography, especially in low-contrast and dense breast images. Different authors have worked in this field for the early detection of cancer using various modalities and segmentation techniques that have been listed in this section for better future research and implementation. The study by Bick et al. [6] implements different procedures, such as thresholding, filtering, and region-growing. The mammogram reduces noise from the image, improves the contrast, and then the texture operator fetches the features. All the pixels in the image are traversed, and then the histogram is used to differentiate between an object and nonobject regions. The region-growing technique is implemented to segment out different areas and then label them, and then morphological filtering is performed on the resultant part to remove the irregularities on curve boundaries. A comparative approach was formulated for various feature extraction methods by Nithya et al. [7] to get a better technique for the identification of tumours. For classification, a supervised neural network was used to select a few features for the study as intensity-based, histogram-based, and grey level co-occurrence matrix features. To segment out doubtful lumps from 70 mammographic images taken from database Mini-MIAS, Anitha et al. [8] worked by updating cellular strength to maximum using cellular automata. Seed selection is made using automation along with histogram peak analysis. The appropriateness of the segmented region is studied. The preprocessing of the image is done before carrying out segmentation. The sensitivity is primarily focused upon during the work. GLCM-based sum average features learned to fetch the seed point automatically, which is considered far better than other GLCM-based texture features. The paper also discussed the importance of extracting the mass boundary more precisely to understand the severity of the tumour. Eltoukhy et al. [9] proposed a technique using a multiscale curvelet transform for the recognition of tumours in the early stage. The coefficient value of the input evaluated and based on the result, and the maximum amount used to alter the information into different scales. The different levels used for the study are 2, 3, 5, 6, and 7, and these are all plotted in vector form. In addition to segmentation, supervised classification method (Euclidean distance measure])is used for better feature classification results. The MIAS database was used for validation purposes. The accuracy of 98.59% raised in a 2- scale and 99% built-in 5- scale. Hariraj et al. [10] have worked on the Mini- MIAS database; preprocessing of the images is done to remove noise and spurious content from the image to improve the quality using the Wiener filter method. K-means cluster techniques used to segment out ROI and KNN and SVM techniques are used to classify the attributes among benign and malignant tissue. The data mining technique is widely used in the paper. The rigorousness of the cancer stage predicted, which may further help in the early detection of cancer. Vala and Baxi [11] discussed the benefits of the Otsu image segmentation method for thresholding the image for automatic ROI segmentation. On paper this method proves to be simple and easy for calculations. The various Otsu methods discussed as thresholding-based improvised histogram, K-means, etc., along with their advantages and disadvantages. This method is mostly used to reduce the complexity of 1-D and 2-D. Agbley et al. [12] and Singh and Veenadhari [13] gave hybrid technology for segmenting out ROI by merging the region and global thresholding applied to the mammographic images. To eliminate Gaussian noise, Wiener filters were used, and then the resulting image was normalized using the histogram to enhance the quality of input images. Among the above two technologies, a global threshold is used to segment ROI, and the segmented region is extracted by region merging. The implementation and testing was done on 50 mammographic images and the specificity of the research was 82%. The related works in tabular form are shown in Table 1.Table 1
Related works in tabular form.
Author and yearAlgorithm implementedWorkingDatasetResearch outcomeAdvantagesAnitha and Peter, 2015 [8]MCSU in cellular automataMammographic image preprocessed to remove noise, markers, pectoral muscles, and unwanted information. Peak analysis is done and enhanced by CLAHE followed by automatic selection of seed and finally updating the cellular strength using cellular automataMini-MIAS, 70 samplesThe specificity of the dataset is maintained and improvedThe initial seed selection needs no manual interception; the automatic pick of seed point is doneGupta and Tiwari, 2017 [14]HM-GRA and CLAHEThe histogram of the mammographic image was generated and the selection of parameters for enhancement was performed. The modification of the histogram is done using the uniform histogram. The grey relational analysis was used to improve the contrast and further normalization and segmentation of ROI was presentedMini-MIAS 322 samplesThe contrast of the image is enhanced to improve minute calcification by decreasing the ratio of false positivesGlobal and local contrast is improved, and sensitivity and specificity both are taken care of at the same timeTaghanaki et al., 2017Geometry-based modelThe maximum area in the breast contour is covered along with the boundary to be marked for early detectionINbreast, DDSM, MIAS, 197, 353, and 322 samplesThe precision of ROI segmentation increasedIt works perfectly with multilayered samples having a lot of variations in intensity and edge boundaryShi et al., 2018 [16]Gradient weight map, pixel wise clustering, and local text filterArtifacts removed from original image further segmentation are done based on pixel-wise clustering, followed by detection of the boundary of breast muscles. Finally, a local texture filter is used to detect calcificationMIAS, BCDR, INbreast, 322, 100, and 201 samplesIt is immune to noises and can detect calcification in dense breasts too. With few settings, FFDM images can also analyze effectivelyThe distinction of skin air boundary marked by the proposed algorithm gradient weight map in compassion to another threshold-based algorithmShen et al., 2018 [17]A genetic algorithm for threshold and segmentation and morphological selectionThe genetic algorithm was implemented to study multilevel threshold, segmentation, and classification based on pectoral muscle segmentation done to classify between successful, acceptable, and unacceptableMIAS, DDSM, and INbreastThe precision of segmentation is higher in comparison to other existing algorithmsThe classification between acceptable, unacceptable, and successful. The sensitivity was then checked for unacceptable samplesHazarika and Mahanta, 2018 [18]Pectoral muscle removal using region growingA suppression algorithm is applied, and further, the samples whose results come comparable and close with the hand-drawn segmented mask are distinguished as acceptedMini-mias and 150 samples86.67% is the accuracy of acceptable and 5.33% for partially fairHand-drawn segmentation mask compare the accuracy of segmentation algorithm givenAlam, et al., 2018 [19]Segmentation using morphologyMorphological and interpolation operations are used to segment ROI, and further splitting is done based on intensity value. For creating clusters of microcalcification area, ranking is used on the differenced imageDDSM, MIAS,248, and 24 samplesThe highest classification accuracy is 94.48% approximateDice metric similarity score was calculated to measure the evaluation, and further reference masks were also usedAnitha and Peter, 2015 [20]KFLS (kernel-based fuzzy clustering)One the preprocessing of the mammogram is done then ROI, which is segmented out using fuzzy C-means clustering segmentation methodDDSM and 300 samples94% segmentation precision in terms of sensitivityAs it is based on an intelligent system, i.e., fuzzy clustering, it provides high precisionTouil and Kalti, 2016 [21]IFBS (iterative fuzzy breast segmentation algorithm)The image is divided intok clusters to remove the over-segmentation background region and extract perfect ROIMIAS and 200 samplesAs compared to the manual ROI curve, its performance is 60% betterIt reduces the over-segmentation of the backgroundKozegar et al., 2018 [22]DRLSE and OBNLM filterRegion growing with combination with GMM is used, followed by despeckling and fine segmentation. DRLSE was modified in the paperUltrasound images and 50 samplesIt assumed that seed position is known before as it does not work on images with edgesAlso, work where the variance is differentAggarwal and Chatha, 2019 [23]Edge detection algorithm is designed on 8-bit grayscale imageBinarization is done to reduce the data reduction step using an edge detection algorithmMIAS 50 random samplesIt reduced the difference between region of interest and backgroundData reduction leads to loss of information can be reduced by using multilevel thresholdingTembhurne et al., 2021 [24]Computer-aided transfer learning-based deep model for binary classification for breast cancer detectionMultichannel merging methods for making a dual-ensemble architectureBreak-his dataset is usedEnsemble architectures by using pretrained models like Xception and InceptionV3 results in an accuracy of 97.5% is achievedCombining different algorithms gives better accuracy over measuring accuracy from one algorithmMalathi et.al., 2021 [25]The algorithm uses breast CAD scheme feature fusion using CNN deep features networkThe abnormality in breast images is scrutinized through deep belief networkCAD images are usedThe outcome shows random forest algorithm is giving an accuracy of around 97.51% over the CNN classifierThe algorithm removes the point spread function where low-dose medical CT image restoration and recovers the reconstructed image quality, efficiency, and speed through sparse transformFang et al., 2021 [26]Configuration of the multilayer perceptron (MLP) neural network multilayer perceptron network using backpropagation networkA new training algorithm is proposed based on whale optimization for MLP networkMIAS 332 digitized mammography imagesDetection performance is detected using detection rate and identification with the false percentageAccuracy is achieved as compared to other methods


## 3. Comparision of Segmentation Techniques for Mammographic Images

There are many works that follow segmentation techniques of masses in mammographic images. Table2 is highlighting the key-points and overview and advantages and major drawbacks of various works. The key objective is to point out the advantages and disadvantages of the various approaches.Table 2
Comparative analysis of various segmentation techniques for mammographic images.
Segmentation techniquesOverviewAdvantagesDrawbacksThresholding [4]This method is based on the threshold maximum and the minimum value, corresponding to different peaks depicting different regions. Various techniques have emerged from high threshold values like a balanced histogram, k-means, and otsu maximum varianceNo prerequisites are required about image and computation is fast with less complexityInformation with low peaks is not considered and ignored; hence, continuous value is not obtained. In the presence of noise and poor contrast, performance is not up to markRegion-based [16, 27]Identical regions are grouped using techniques like region growing, splitting, merging, etcBetter than edge detection about noise immunity. It works better in homogeneous regionsQuite expensive in the context of both time and memoryClustering [10]It creates different clusters in the spatial domain. Groups are homogeneousBest results on overlapped data. Results best for classification. Suitable for real-life applications as it uses fuzzy logicClustering validity is a challenging task to be determined. Expensive and sensitive to primary clustersEdge detection [22]Work on discontinuity principle and locate regions with minimum sudden change. It is of two types sequential and parallelResults are better with images having a high contrast valueGive unexpected results with images having many edges or improperly defined boundaries and noisy imagesContour-based segmentation [25]In this work computer-aided diagnosis (CAD) and fusion via CNN is done for recognition, analysis, and further treatment with the help of RF giving maximum accuracy of 97.51% and minimum error via CNN classifier 95.65%. After segmentation using sparse transform, the algorithm eliminates the attributes of the point spread functionDiagnosis of cancer and accuracy detection is now upgraded with machine learning techniques. This work offers enhanced performance and better implementation results, with accuracy and lesser time in medical CT image restoration, new image recovery with quality and speedThe present CAD system for measuring the accuracy is not recognized and acceptableEnergy function-based technique [28]It is based on parameterizing the curve, taking some sampling valuesMinimum processing is required and if flexible. Fast and efficientIt is not useful in the case of higher dimensions, selection of sampling strategy, topology changes, etcBreast-region segmentation [29]This segmentation has three divisions: Classical segmentation, which includes region, threshold, edge-based segmentation utilizing supervised and unsupervised methods using deep learningU-net was chosen because, unlike other deep learning models, it does not require annotated photosThe method is easily simulated by AI, ML, or DNN persons rather than physicians or biologistsOptimized-region growing technique [30]The optimal features are selected by a hybrid optimization algorithm and classified using a neural networkThe velocity updated lion algorithm combines the lion algorithm (LA) with PSO to achieve the best feature selection and weight optimization using NN (VU-LA) and VU-LA is also compared to existing models such as the whale optimization algorithm, grey wolf optimization, firefly, PSO, and LA in terms of performanceThe algorithm cannot work well on hazy or faint imagesRegion of interest [31]After segmentation to obtain a region of interest (ROI), the images were cleaned up using a median filter and compared using ANN, SVM, and reduced features of SVMThe statistics and grey level cooccurrence matrix are utilized to classify to extract the features from enhanced images using the hybrid SVM-ANNA better algorithm can be made for enhanced feature detectionSupervised segmentation [32]The work proposes to constrain the segmentation output when morphological operations to measure performance which uses top-hat and closing operations to evaluate on high-resolution images from the INBreast datasetIt achieves an increase in F1 and in the recall if compared to the training without morphology lossThe evaluations cannot be justified sometimes when images are taken from different sourcesPseudo-color segmentation [33]Thermal cameras record radiation images, which are then transformed to images of pseudo-colored. All the colors of the thermogram correspond to a specific temperature. The interpretation of breast thermograms is mostly dependent on color analysis and thermogram asymmetry analysis. The work analyses breast thermograms by segmenting the RoI, extracted as a hot region, and then analyzing the color. Abnormalities are shown in the hottest regions by contoursThe results compared to diagnosis to ensure infrared thermography is a reliable tool for detecting breast cancerSometimes pathologists fail to identify the raw image received by the radiationGraph segmentation [34]The automatic segmentation of stained tissue images aid in the detection of malignant disease, and this is done after the separation of contacting cells. Traditional segmentation algorithms face numerous challenges. They present a novel automatic approach for segmenting clustered cancer cells in this work. In the first stage, also use Chan-Vese energy functional to determine cell areas by a modified geometric active contourBy determining high concavity locations along with the cell outlines, contacting cell areas are recovered from the presegmented imageImage profiles can sometimes fail to subclassify breast tumours into additional subtypes, which can help in diagnosis and survivalVariant feature transformation [35]To subclassify extremely aggressive breast malignancies, the researchers used public transcriptomics datasets in breast cancer cell lines and breast cancer tumours, and associated splice variantsSplicing is becoming more often used as a biomarker for grading tumoursSometimes splicing may not be an exact method to classify their variantsRegion –growing algorithm [36]The work provides a new CRG (conditional region growth) approach for determining correct MC bounds beginning from seed points selected, and they are determined by detecting regional maxima and analyzing superpixels. The region-growing stage is then maintained by the criteria set tuned to MC detection for contrast and shape variation obtained from prior knowledge, which determines the size of the searching area in the neighbourhood. To do qualitative and quantitative analysis for detection of MC and delineation many experiments are done on MC of multiple typesThe importance of utilized criteria in the context of MC delineation for better management of breast cancer is demonstrated by a comparison of the proposed technique state-of-the-artMicrocalcifications with their morphology are the indicators of breast cancer when shape and size are considered when malignity degree is to be found out and therefore delineation of MC is done for diagnosis of cancerGraph-cut algorithm [37]The mammograms can be classified with BIRADS which subjects to qualitative assessments and face inter-reader and intra-reader variations. Comparative analysis of various articles is done for classification accuracy and computational complexity to design an algorithm for the measurement of breast density using machine learning or deep learningBreast density measurement via machine learning and deep learning is increasing the rapid developmentIf the density of the breast increases, then chances of breast cancer also increase which reduces the sensitivity of measuring mammographic densityWatershed algorithm [38]A novel approach for detection of breast cancer automatically from histopathological images that are composed of binary and eight-class based on a convolutional-LSTM learning model trained on BreakHis dataset and preprocessing using marker-controlled watershed segmentation algorithm and optimized SVM classifier. When MWSA is used with the optimized SVM classifier with Bayesian optimization processed HPIs improve when compared to the CLSTM model’s softmax classifierWhen compared to present approaches utilizing BreakHis dataset, the methodology achieves great performance for both classificationsSometimes histopathological images cannot be classified and may not detect breast cancer, therefore, AI and deep learning-based applications are used for automated breast cancer detection with high performancesFuzzy –C means [39]This method uses computer-aided cancer diagnosis in its various stages. The first stage performs a reduction of noise and preprocessing contrast enhancement, while the 2nd stage segments the ROI and, the kernel fuzzy C-means method is used for segmentation, and features are extracted and feature selection is employed. For final identification, these selected features are fed into an SVMThe proposed version of the metaheuristic neural network optimization approach improves feature selection and SVM classifier performance. When the methodology was compared against five state-of-the-art methodologies, results revealed the approach was superiorSometimes Computer-aided design lacks when input is not appropriateOtsu’s optimal thresholding [40]The paper creates a framework that scans the stage of cancer by using the optimized kernel fuzzy clustering algorithm to determine cancer and identify segmented regions in mammogram images. The mammographic images are preprocessed noise-free images obtained by using the hybrid denoising filter algorithm. Data clustering is done to classify data of similar types in one group and of dissimilar types in another groupResults give accuracy & efficiency of the proposed system compared to methods such as K-means, OKFCA, and otsuIssues like mammograms deviate artifacts, are similar breast tissues and contrast issues on the boundary between skin and airFusion of K-means and region growing algorithms [41]An automated system used by radiologists for diagnostic decisions involving detection of breast masses by using an optimized region growing method, optimal seed point selection, and threshold generation were achieved using grey wolf optimization. The features for both global and local are extracted for shape features, grey level: co-occurrence matrix, run length matrix, texture feature: Local binary pattern, and scale-invariant feature transformAn amalgamation of local and global features with an SVM classifier differentiates benevolent or malignant images and an accuracy of 96% is achieved by GLCM and LBPThe existence rate after detection of cancer affected person cannot be sometimes predicted


## 4. Proposed Methodology

Image segmentation refers to the techniques of dividing an image into different regions. The most effective method to analyze anatomical structure in medical is “region growing method” [42, 43]. But it does not give proper and more accurate results if it directly applies to the input images that are having noisy and low contrast. We have proposed algorithm could be applied on the mammographic images more effectively in such condition.The proposed method developed to conduct the segmentation of mammograms is detailed in the flowchart shown in Figures1 and 2.Figure 1
Proposed method for doing segmentation on mammograms.Figure 2
Diagram of the proposed method of classification using CNN models on mammograms.The algorithm of the implemented work, is as below (Algorithm 1):Algorithm 1: Process to design a Graphical User Interface of the proposed method
To design a Graphical User InterfaceIM = imresize(I,.3)//Input an image by renaming it as IM and resizing it if required.GR= ImageIn(:,:,3);//Apply Complement using the green channel on input image IM.GRC = imgcomplement(GR);axxes(handlesi.greenimg_channel);set(imgshow(GRC));CLH = adaptohisteq(GRC);//To apply CLAHE on GRC to receiveCLH % contrast limited image.set(imshow(CLH));SE = strel(“ball”,8,8);//Perform structuring of an element with the specified neighbourhood (8).mgopen = imgopen(CLH,SE);//Do morphological on binary image CLH with structuring element ‘sse’.gordisk = CLH - gimgopen;//Replace optic disk by appling a 2D Median Filter.medfillt = medfillt2(gordisk);backgroundimg = imgopen(medfillt, strell(“disk”,160));IM2 = GC1;//Eliminate background for adjustment of image to retrieve GC1.IM2 = double(IM2);//The I/P image (GC1) by using Fuzzy C-Means, does image segmentation.//Execute the above segmentation to construct a flat structure element with in the specified neighbourhood.backgroundimg = imopenimg(IMMM, strell(“disk”, 46));//Eliminate all connected components having less than 40 pixels to create new binary image I5 from a binary image and is called as an area openingI5=IMMMM-backgroundimg;I5 = bwareaopenimg(I5,30);axess(handless.segmented_img);//Open an axis at the specified position and return a handle to it.set(imshowimg(I5));set(LTprojectt.segmented_img,“Userdata”,I5);ffcmm1=(['The value of Cluster1 = 'num2str(cccc1)]);//Retrieve the final image I5 to find cluster.ffcmm2=(['The value of Cluster2 = 'num2str(cccc2)]);classIM_1 = Imgg(:);//Find image vectors of input image (IM) and segmented image (I5)classI5_2 = Imgg1(:);//To detect errors set all default parameters//Evaluate the threshold values among the data points.% Sort data points %ss_data = unique(sort([class_1; class_2]));% Del NaN values %ss_data(isnan(ss_data)) = [];% Cal difference between consecutive points %dd_data = diff(ss_data);% Cal last point %dd_data(length(d_data)+1,1) = dd_data(length(d_data));% Cal first point %thresh(1,1) = ss_data(1) - dd_data(1);% Cal Threshold %thres(2:len(s_data)+1,1) = s_data + d_data./2;cur = zeross(sizeof(thresh,1),2);//Find sensibility and specificity of every threshold valuedis = zeross(sizeof(thresh,1),1);for idd_t = 1:1:len(thresh)TruePositive = len(find(class2≥thresh(idd_t)));FalsePositive = len(find(class1 ≥ thresh(idd_t)));FalseNegative = len(find(class2 <thresh(idd_t)));TrueNegative = len(find(class1 <thresh(idd_t)));S = TruePositive/(TruePositive + FalseNegative);SP = curve(idd_t1,2) = TrueNegative/(TrueNegative + FalsePositive);//Calculate distance between every point and optimum point ranging [0,1]distance(idd_t1) = sqrt((1-curve(idd_t1,1))^2+(curve(idd_t1,2)-1)^2);Calculate the best value for threshold position, Sensitivity, Specificity, Area under curve, Accuracy, all false and true positives and negatives.


## 5. Experiment and Results

The mean based region growing segmentation (MRGS) method [44] is presented which has the improvement over ordinary region growing (RG) method with regard to the selection of threshold.Figure3 shows the first image of MIAS database “mdb001.pgm” given as input to the developed method. The obtained images by applying different approaches displayed under relevant captions within the frame.Figure 3
First image of the MIAS database “mdb001.pgm” undergoing segmentation.Figure4 shows the second image of the MIAS database “mdb002.pgm” being given as an input to the developed method.Figure 4
Second image of the MIAS database “mdb002.pgm” undergoing segmentation.Figure5 shows the third image of the MIAS database “mdb003.pgm” been given as an input to the developed method.Figure 5
Third image of the MIAS database “mdb003.pgm” undergoing segmentation.Figure6 shows the fourth image of the MIAS database “mdb004.pgm” been given as an input to the developed method.Figure 6
Fourth image of the MIAS database “mdb004.pgm” undergoing segmentation.The images in Figures7 and 8 are classified images out of the pixels, combined using PYPLOT and bypassing the input data through 3 layered CNN models with alternated max pool layers to combine the pixels of similar density. They used ReLu activation function in the output layer after flattening the dataset with the dropout to prevent the NN overfitting in the predictions. On the predictions, the original input is conserved, and pixels are combined using PYPLOT to create a visual image of the flattened input data to review the visualized image and the output.Figure 7
Image of the MIAS database undergoing classification for finding out the benign and malignant images.Figure 8
Malignant images, as well as benign images of all image sets of the MIAS database undergoing classification to find the benign and malignant image.The first fifteen images from the MIAS database were taken for performing segmentation of mammograms, and the readings were obtained on sixteen different parameters, as shown in Table3.Table 3
Observations of various parameters on different images from the MIAS database.
Image nameDistanceThresholdSensitivitySpecificityARoCAccuracyPPVNPVFNRFPRFDRFORF1 scoreMCCBMMKmdb001.pgm0.91012190.08991.00000.37100.92450.99480.92390.91010.0000.00520.07610.16490.28730.08980.9187mdb002.pgm0.89152170.10850.99980.38930.92590.98040.92540.89150.00020.01960.07460.19530.31320.10830.9057mdb003.pgm0.91932120.08070.99700.36360.92100.71130.92300.91930.00300.28870.07700.14490.22200.07770.6343mdb004.pgm0.87712170.12290.99980.40060.92700.98260.92650.87710.00020.01740.07350.21840.33390.12270.9091mdb005.pgm0.77492160.22510.99960.48080.93540.98220.93450.77490.00040.01780.06550.36620.45390.22470.9166mdb006.pgm0.75252160.24750.99960.49300.93720.98370.93620.75250.00040.01630.06380.39540.47680.24710.9200mdb007.pgm0.86492150.13510.99930.40840.92760.94410.92740.86490.00070.05590.07260.23640.34220.13440.8715mdb008.pgm0.84542110.15460.99580.42270.92600.76970.92870.84540.00420.23030.07130.25750.32410.15040.6984mdb009.pgm0.85042180.14960.99990.41580.92940.99310.92860.85040.00010.00690.07140.26010.37130.14960.9216mdb010.pgm0.89332180.10670.99990.38170.92580.99030.92520.89330.00010.00970.07480.19260.31230.10660.9155mdb011.pgm0.84812180.15190.99990.41750.92950.99320.92870.84810.00010.00680.07130.26350.37410.15180.9219mdb012.pgm0.85312160.14690.99960.41420.92890.97290.92830.85310.00040.02710.07170.25530.36340.14660.9012mdb013.pgm0.85692110.14310.99580.41110.92510.75570.92280.85690.00420.24430.07220.24060.30810.13890.6835mdb014.pgm0.85172160.14830.99960.41790.92900.97320.92840.85170.00040.02680.07160.25740.36530.14800.9016mdb015.pgm0.88252140.11750.99870.38790.92560.89290.92600.88250.00130.10710.07400.20760.30840.11620.8189


## 6. Conclusion and Future Scope

This paper discussed the method for performing the segmentation of mammograms. More than fifteen images of the MIAS database are tested to assure the worth of the conducted research work. The undertaken research work proved that the combined approaches provide improved segmentation accuracy. Accuracy related to segmentation has a vital role in categorizing cancer as benign or malignant. The adopted preprocessing methods assist in procuring enhanced segmentation outcomes. In future work, images from different databases are used to perform segmentation, and the number of relevant parameters (distance, sensitivity, specificity, ARoC, accuracy, PPV, NPV, FNR, FPR, FDR, FOR,F1 Score, MCC, BM, and MK) increased. Even other types of breast images bearing different properties are used, such as ultrasound and thermography. The model can be more optimized with PCA or applying SVM at the output layer for confident results, and we can say that images can produce a huge number of dimensions. So, we can limit the dimensions with PCA with a minute compromise in accuracy but optimize code. The proposed model underwent different steps detect all the errors, evaluate the threshold values among the data points, find the sensibility and specificity of every threshold value, and also calculate the best value for threshold position, sensitivity, specificity, area under curve, accuracy, and all false and true positives and negatives. Later, the classification is done for finding out the benign and malignant images. The proposed model helps in detecting breast cancer, which reduces the need for breast removal and also the need of chemotherapy, saving the lives at earlier stage.



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