Performance Analysis of Breast Cancer Classification with Softmax Discriminant Classifier and Linear Discriminant Analysis

To facilitate the enhanced reliability of Raman-based tumor detection and analytical methodologies, an ex vivo Raman spectral investigation was conducted to identify distinct compositional information of healthy (H), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). Then, principal component analysis-linear discriminant analysis (PCA-LDA) and principal component analysis-support vector machine (PCA-SVM) models were constructed for distinguishing spectral features among different tissue groups. Spectral analysis highlighted differences in levels of unsaturated and saturated lipids, carotenoids, protein, and nucleic acid between healthy and cancerous tissue and variations in the levels of nucleic acid, protein, and phenylalanine between DCIS and IDC. Both classification models were principal component analysis-linear discriminant analysis to be extremely efficient on discriminating tissue pathological types with 99% accuracy for PCA-LDA and 100%, 100%, and 96.7% for PCA-SVM analysis based on linear kernel, polynomial kernel, and radial basis function (RBF), respectively, while PCA-SVM algorithm greatly simplified the complexity of calculation without sacrificing performance. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.

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Deep Learning Based Analysis of Breast Cancer Using Advanced Ensemble Classifier and Linear Discriminant Analysis

IEEE Access ◽

10.1109/access.2020.3005228 ◽

2020 ◽

Vol 8 ◽

pp. 120208-120217 ◽

Cited By ~ 1

Author(s):

Xinfeng Zhang ◽

Dianning He ◽

Yue Zheng ◽

Huaibi Huo ◽

Simiao Li ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Ensemble Classifier ◽

Linear Discriminant

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Linear discriminant analysis and support vector machines for classifying breast cancer

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v10.i1.pp253-256 ◽

2021 ◽

Vol 10 (1) ◽

pp. 253

Author(s):

Zuherman Rustam ◽

Yasirly Amalia ◽

Sri Hartini ◽

Glori Stephani Saragih

Keyword(s):

Breast Cancer ◽

Support Vector Machines ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Support Vector ◽

Breast Cancer Dataset ◽

Cancer Dataset ◽

Linear Discriminant ◽

Specialist Doctor ◽

Vector Machines

<span id="docs-internal-guid-4db59d91-7fff-c659-478a-6dd7456f380f"><span>Breast cancer is an abnormal cell growth in the breast that keeps changed uncontrolled and it forms a tumor. The tumor can be benign or malignant. Benign could not be dangerous to health and cancerous, but malignant could be has a probability dangerous to health and be cancerous. A specialist doctor will diagnose the patient and give treatment based on the diagnosis which is benign or malignant. Machine learning offer times efficiency to determine a cancer cell. The machine will learn the pattern based on the information from the dataset. Support vector machines and linear discriminant analysis are common methods that can be used in the classification of cancer. In this study, both of linear discriminant analysis and support vector machines are compared by looking from accuracy, sensitivity, specificity, and F1-score. We will know which methods are better in classifying breast cancer dataset. The result shows that the support vector machine has better performance than the linear discriminant analysis. It can be seen from the accuracy is 98.77%.</span></span>

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