A Comparative Study of Breast Cancer Diagnosis Using Supervised Machine Learning Techniques

Gupta, Madhuri; Gupta, Bharat

doi:10.1109/iccmc.2018.8487537

Cited by 59 publications

(22 citation statements)

References 22 publications

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“…D. song et al [ 31 ] applied a deep neural network for breast cancer prognosis prediction from multidimensional data and achieved a specificity of 99%. Multilayer perceptron (MLP) is a feed-forward neural network class that has three layers: the input layer, the hidden layer, followed by the output layer, which was compared [ 32 ] with other algorithms to improve the model accuracy for the prediction of breast cancer from fine-needle aspiration. Xin Shu et al [ 33 ] proposed a region-based pooling structure deep neural network for mammogram image classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Rehman

Pei

et al. 2021

Sensors

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Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Rehman

Pei

et al. 2021

Sensors

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“…Paper [16], given by Madhuri Gupta and Bharat Gupta they used machine learning algorithms to the Wisconsin dataset. The algorithms they used are linear regression, Random Forest, Multilayer perceptron and Decision Tree.…”

Section: Literature Surveymentioning

confidence: 99%

RFSVM: A Novel Classification Technique for Breast Cancer Diagnosis

Soni*¹,

Bora²,

Ghosh³

et al. 2019

IJITEE

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Cancer is a disease, which develops, in human body due to gene mutation. Due to various factor cells turn into cancerous cell and grow rapidly while damaging normal cells. Many women get affected by breast cancer, which might even cause death if not treated at early stage. Early detection of breast cancer is highly important to increase the survival rate. Machine learning methods and technologies are making it possible to classify and detect the class in an accurate manner. Among other classifiers, random forest and support vector machine are two classifiers that have a good classification power. In this, research a combination of these two classifier i.e. Random Forest and Support Vector Machine (RFSVM) is proposed for early diagnosis of breast cancer cell using Wisconsin Breast Cancer Dataset (WBCD). Using different train-test data ratio experiments are performed and an average of more than 98percentage accuracy is achieved using this hybrid classifier. This paper overcomes the over-fitting problem of random forest and the need of tuning the parameters of Support Vector Machine. Even with limited data available, the classifier tunes its parameters so well to give a highly accurate result.

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“…Over fitting can be prevented by using this random forest algorithm. [10] Several possibilities of decision trees can be used to determine which features provides more suitable to determine the labels which leads to conclude whether the inputs(features) predict the genes which is pediatric cardiomyopathy associated gene or not. (Yes/No).…”

Section: Random Forestmentioning

confidence: 99%

A Prediction of Pediatric Cardiomyopathy Disease Associated Genes using Machine Learning Algorithms

2019

IJRTE

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Pediatric cardiomyopathy is considered as one of the heart diseases, which causes by abnormal disorder of the heart muscle. If pediatric cardiomyopathy remains untreated and unidentified at the early stages, it leads to heart failure. The global number of deaths and disability attributed to cardiomyopathy has steadily increased. Hence, machine learning approaches can solves the problem of identifying the critical problem by determining the pediatric cardiomyopathy disease associated genes from the collection of differentially expressed genes that are recognized by biological process of genes. The main objective of this study is to design a machine learning model which can predict the likelihood of pediatric cardiomyopathy in genes specified biological features with maximum of accuracy. Identified high throughput machine learning algorithms like Logistic Regression, Naive Bayes, Random Forest, and Support Vector Machine were used in this experiment to determine the genes which can be derived from internal database repository having biological process of genes specified. Experiments are conducted on Gene Expression Omnibus (GEO) datasets which sourced from cardiogenomics.org and Biohunter tool. The performance of these machine learning algorithms is evaluated on various measures like Accuracy, Precision, Recall, F-Measure, and Receiver Operating Characteristics (ROC). From the obtained results shows that Random Forest provides high accuracy 84.4% when compared to other four machine learning algorithms.

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A Comparative Study of Breast Cancer Diagnosis Using Supervised Machine Learning Techniques

Cited by 59 publications

References 22 publications

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

RFSVM: A Novel Classification Technique for Breast Cancer Diagnosis

A Prediction of Pediatric Cardiomyopathy Disease Associated Genes using Machine Learning Algorithms

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