Soft Classification Techniques for Breast Cancer Detection and Classification

Ivaturi, Anjana; Singh, Ankita; Gunanvitha, B.; Chethan, K S

doi:10.1109/iciem48762.2020.9160219

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…As first option typical model parameters' [12] 98 ---LR [12] 97.23 ---LDR [12] 95.73 ---KNN [12] 94.73 ---NB [12] 93.46 ---DT [12] 91. best training performance curves (see Fig. 3-6), and the area under the curve (AUC) (see Table 3).…”

Section: Validation Methodsmentioning

confidence: 99%

“…In [20], two machine learning techniques, namely Naive Bayes and the K-Nearest Neighbor (KNN) were evaluated on the Wisconsin breast cancer dataset for the tumor classification purpose and compared their performance as KNN achieving 97.51% with the least error rate while NB classifier having 96.19 % accuracy. For breast cancer classification, [12] implemented six machine learning techniques including Support Vector Machine (SVM), Decision Tree Classifier, Naive Bayes, Logistic Regression, Linear Discriminant Analysis, and K Nearest Neighbor, and achieved the highest classification accuracy of 98% with support vector machine through 3-fold cross-validation method. Stacked Autoencoders with sparsity constraints have observable effects in feature learning and classification while using different hidden units.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, the Deep Neural Network (DNN) based algorithms in the field of medical diagnosis have gained attractiveness due to their exceptional performance in challenges. In the majority of artificial intelligence-based research articles for breast cancer classification tasks [9][10][11][12][13][14][15][16][17][18], the model performance evaluation was performed on either breast cancer images taken through different computer-based techniques or numerical features' dataset extracted from analyzed biopsy sample cell nuclei.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier

Munir¹,

Aslam

Shafique³

et al. 2022

murjet

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Breast cancer is among one of the non-communicable diseases that is the major cause of women's mortalities around the globe. Early diagnosis of breast cancer has significant death reduction effects. This chronic disease requires careful and lengthy prognostic procedures before reaching a rational decision about optimum clinical treatments. During the last decade, in Computer-Aided Diagnostic (CAD) systems, machine learning and deep learning-based approaches are being implemented to provide solutions with the least error probabilities in breast cancer screening practices. These methods are determined for optimal and acceptable results with little human intervention. In this article, Deep Stacked Sparse Autoencoders for breast cancer diagnostic and classification are proposed. Anticipated algorithms and methods are evaluated and tested using the platform of MATLAB R2017b on Breast Cancer Wisconsin (Diagnostic) Data Set (WDBC) and achieved results surpass all the CAD techniques and methods in terms of classification accuracy and efficiency.

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Section: Validation Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier

Munir¹,

Aslam

Shafique³

et al. 2022

murjet

View full text Add to dashboard Cite

show abstract

“…To increase their sensitivity and specificity, these algorithms undergo extensive training on big datasets. 1.8 Patient Empowerment: In addition to empowering medical professionals, AI may also empower patients by giving them access to informational materials, social networks, and self-care tools [8].…”

Section: Research and Developmentmentioning

confidence: 99%

Early Prediction of Breast Cancer Risks Using Convolutional Neural Network

Kumari,

Kumar

2023

IJIMSR

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Breast Cancer is one of the deadliest diseases in the world, among cancer categories, early detection is essential to better treatment outcomes and survival rates. Delay in diagnosis due to medical mistakes or incorrect interpretation of patient reports might have a negative effect on patient outcomes.Artificial intelligence (AI), a component of contemporary technology, has demonstrated encouraging potential for helping medical practitioners to increase the precision and effectiveness of cancer diagnosis. AI programs can be trained to examine several kinds of medical imaging data, including MRI and CT scans, and help spot anomalies or malignant cells. The study aims to establish a model for estimating the risk of breast cancer based on a combination of medical and behavior-related risk factors.The goal is to identify high-risk groups of patients who have undergone breast cancer surgery so that they can receive closer monitoring and more accurate screening.In this proposed work,we try to use CNN Model for classifying the risk of breast cancer.

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Machine Learning for Breast Cancer Diagnosis and Classification Using Hand-Crafted Features

Dong

Kandel

2021

Advances in Intelligent Systems and Computing

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Soft Classification Techniques for Breast Cancer Detection and Classification

Cited by 11 publications

References 16 publications

Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier

Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier

Early Prediction of Breast Cancer Risks Using Convolutional Neural Network

Machine Learning for Breast Cancer Diagnosis and Classification Using Hand-Crafted Features

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