Machine Learning Techniques for Improved Breast Cancer Detection and Prognosis—A Comparative Analysis

2021 ◽  
pp. 441-455
Author(s):  
Noushaba Feroz ◽  
Mohd Abdul Ahad ◽  
Faraz Doja
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals A Breast Cancer Detection Using Image Processing and Machine Learning Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 5250-5256
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Image Processing ◽  
Early Detection ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Image Processing Technique ◽  
Learning Techniques

Routine breast cancer screening allows the disease to be diagnosed and treated prior to it causing noticeable symptoms. During the diagnosis process there are chances of wrong detection hence a less human interfaced system has to be developed, hence the goal of breast cancer detection using machine learning techniques is used to find it before it spreads to the larger extent. Screening refers to tests and exams used to find a disease in people who don’t have any symptom. Early detection means finding and diagnosing a disease earlier than waiting for symptoms to start causing the effect on the neighboring cells. The breast cancer is the second most death causing cancer in humans, one in every ten women are affected by the breast cancer. Breast cancer is not only affecting the women. Men are also prone to get affected by the breast cancer but in smaller rates because of the absence of milk ducts and other lobules related to women. Early detection of the breast cancer helps in reducing the death rates if treated earlier and by proper diagnosis. In this paper the discussion of the various image processing technique done on the image and the CNN, SVM algorithm implementation on dataset images for the classification of malignant and non malignant cells are used and various tests were performed using different other machine learning algorithms and there level of accuracy and difference of various parameters are discussed for image processing MATLAB coding is used.

scholarly journals A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 111 ◽  
Author(s):  
Muhammet Fatih Ak
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Visualization ◽  
Cancer Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Cancer Types ◽  
Detection And Diagnosis ◽  
Using Data

In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.

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