Breast cancer mass localization based on machine learning

Qasem, Ashwaq; Abdullah, Siti Norul Huda Sheikh; Sahran, Shahnorbanun; Wook, Tengku Siti Meriam Tengku; Hussain, Rizuana Iqbal; Abdullah, Noor Rain; Ismail, Fuad

doi:10.1109/cspa.2014.6805715

Cited by 19 publications

(7 citation statements)

References 16 publications

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“…In 2014, Qasem et al [ 90 ] proposed the radial basis function (RBF) kernel for breast cancer mass identification in the images. For the diagnosis of breast cancer, first of all apply segmentation algorithm across breast US images.…”

Section: Future Trends Based On the Supervised Learning Methodsmentioning

confidence: 99%

Automated Techniques for the Interpretation of Fetal Abnormalities: A Review

Rawat¹,

Jain²,

Shrimali³

2018

Applied Bionics and Biomechanics

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Ultrasound (US) image segmentation methods, focusing on techniques developed for fetal biometric parameters and nuchal translucency, are briefly reviewed. Ultrasound medical images can easily identify the fetus using segmentation techniques and calculate fetal parameters. It can timely find the fetal abnormality so that necessary action can be taken by the pregnant woman. Firstly, a detailed literature has been offered on fetal biometric parameters and nuchal translucency to highlight the investigation approaches with a degree of validation in diverse clinical domains. Then, a categorization of the bibliographic assessment of recent research effort in the segmentation field of ultrasound 2D fetal images has been presented. The fetal images of high-risk pregnant women have been taken into the routine and continuous monitoring of fetal parameters. These parameters are used for detection of fetal weight, fetal growth, gestational age, and any possible abnormality detection.

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Section: Future Trends Based On the Supervised Learning Methodsmentioning

confidence: 99%

Automated Techniques for the Interpretation of Fetal Abnormalities: A Review

Rawat¹,

Jain²,

Shrimali³

2018

Applied Bionics and Biomechanics

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“…is calculated based on three diversity pair wised measures, , correlation coefficient and double-fault as follow: (14) where, , and are the average values of the , correlation coefficient and double-fault respectively and could be calculated as: is the number of instance in the testing data that are incorrectly classified by both classifier ; is the number of instance in the testing data that are correctly classified by classifier and misclassified by classifier , and is the number of instance in the testing data the misclassified by classifier and correctly classified by classifier .…”

Section: ) Ba For Ensemble Pruningmentioning

confidence: 99%

“…In image segmentation stage, a mammogram will be segmented to extract Region of Interest (ROI). ROI maybe extracted manually [4]- [9] or it can be extracted automatically using any segmentation method [10]- [14]. The second stage is the feature extraction, which plays an important role for achieving high performance in the classification stage.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Ensemble Pruning based on Bee Algorithm for Mammogram Classification

Qasem

Abdullah

Sahran

et al. 2018

ijacsa

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In mammogram, masses are primary indication of breast cancer; and it is necessary to classify them as malignant or benign. In this classification task, Computer Aided Diagnostic (CAD) system by using ensemble learning is able to assist radiologists to have better diagnosis of mammogram images. Ensemble learning consists of two steps, generating multiple base classifiers and then combining them together. However, combining all base classifier in the ensemble model increases the computational cost and time. Therefore, ensemble pruning is an important step in ensemble learning to select the ensemble's members. Due to huge subsets of combination in the ensemble, selecting the proper ensemble subset is desirable. The objective of this paper is to select the optimal ensemble subset by using Bee Algorithm (BA). A pool of different classifier models such as Support vector machine, k-nearest neighbour and linear discriminant analysis classifiers have been trained using different samples of training data and 12 groups of features. Then, by using this pool of classifier models, BA was used to exploit and explore random uniform combination subsets of the trained classifiers. As a result, the best subset will be selected as the optimal ensemble. The mammogram image dataset that was used to test the model has been collected from Hospital Kuala Lumpur (HKL) and consists of 263 benign and malignant masses. The proposed method gives 77 % of Area Under Curve(AUC), 83% of accuracy, 93% of specificity and 60% of sensitivity.

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“…In breast cancer, mammogram, which is the input image of CAD system, will be reviewed by radiologist after CAD segmented it to identify the suspicious region [8]. The importance of analysing mammogram using CAD to detect breast cancer in early stage has been proven by earlier researchers [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…For those reasons, machine learning is using in CAD system to improve it in dealing with mammogram data. Thus, it is highly useful in cancer detection because it can learn from past examples [9,10,11]. Breast cancer medical data is not only the mammogram but it also large information about patients describe their medical condition.…”

Section: Introductionmentioning

confidence: 99%

Breast Tissue Classification via Interval Type 2 Fuzzy Logic Based Rough Set

Baharuddin

Abdullah

Sahran

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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BIRADS is a Breast Imaging, Reporting and Data System. A tool to standardize mammogram reports and minimizes ambiguity during mammogram image evaluation. Classification of BIRADS is one of the most challenging tasks to radiologist. An apt treatment can be administered to the patient by the oncologist upon acquiring sufficient information at BIRADS stage. This study aspired to build a model, which classifies BIRADS using mammograms images and reports. Through the implementation of type-2 fuzzy logic as classifier, an automatically generated rules will be applied to the model. To evaluate the proposed model, accuracy, specificity and sensitivity of the modal will be calculated and compared vis-à-vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department, Hospital of National University of Malaysia (UKM). The data was initially processed to remove noise and gaps. Then, an algorithm developed by selecting type-2 fuzzy logic using Mamdani model. Three types of membership functions were employed in the study. Among the rules that used by the model were obtained from experts as well as generated automatically by the system using rough set theory. Finally, the model was tested and trained to get the best result. The study shows that triangular membership function based on rough set rules obtains 89% whereas expert rules achieve 78% of accuracy rates. The sensitivity using expert rules is 98.24% whereas rough set rules obtained 93.94%. Specificity for using expert rules and rough set rules are 73.33%, 84.34% consecutively. Conclusion: Based on statistical analysis, the model which employed rules generated automatically by rough set theory fared better in comparison to the model using rules given by the experts.

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Breast cancer mass localization based on machine learning

Cited by 19 publications

References 16 publications

Automated Techniques for the Interpretation of Fetal Abnormalities: A Review

Automated Techniques for the Interpretation of Fetal Abnormalities: A Review

Heterogeneous Ensemble Pruning based on Bee Algorithm for Mammogram Classification

Breast Tissue Classification via Interval Type 2 Fuzzy Logic Based Rough Set

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