Surrogate-assisted firefly algorithm for breast cancer detection

2021 ◽  
pp. 1-12
Author(s):  
Wenhua Zhu ◽  
Hu Peng ◽  
Chaohui Leng ◽  
Changshou Deng ◽  
Zhijian Wu
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Firefly Algorithm ◽  
Severe Disease ◽  
Breast Cancer Detection ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Computation Cost ◽  
Women Health ◽  
Efficient Detection

Breast cancer is a severe disease for women health, however, with expensive diagnostic cost or obsolete medical technique, many patients are hard to obtain prompt medical treatment. Thus, efficient detection result of breast cancer while lower medical cost may be a promising way to protect women health. Breast cancer detection using all features will take a lot of time and computational resources. Thus, in this paper, we proposed a novel framework with surrogate-assisted firefly algorithm (FA) for breast cancer detection (SFA-BCD). As an advanced evolutionary algorithm (EA), FA is adopted to make feature selection, and the machine learning as classifier identify the breast cancer. Moreover, the surrogate model is utilized to decrease computation cost and expensive computation, which is the approximation function built by offline data to the real object function. The comprehensive experiments have been conducted under several breast cancer dataset derived from UCI. Experimental results verified that the proposed framework with surrogate-assisted FA significantly reduced the computation cost.

scholarly journals Breast Cancer Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 2358-2360
Author(s):  
Jain Aditi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cause Of Death ◽  
Cancer Detection ◽  
Web Application ◽  
Breast Cancer Detection ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Common Cause ◽  
Learning Language

Breast Cancer is a global problem currently. It is a disease which is a common cause of death for women worldwide. Earlier doctors used Mammography to find out that whether the person is suffering from it or not. But sometimes even Mammography was not able to detect whether the result is a yes or a no. for the detection of breast cancer, Machine Learning language can do wonders. Hence, we are making a web application which will tell whether the person is suffering from Breast Cancer in just a minute by giving the input. The input we have used is the Wisconsin Breast Cancer Dataset (WBCD). This web application will be very helpful for doctors and radiologists. Initially, we researched about Breast Cancer and its different treatments. But could not get any idea related to its detection process as any process did not surety. Hence, we came up with the Breast Cancer Detection Web Application.

scholarly journals BREAST CANCER DETECTION USING MAMMOGRAM FEATURES USING RANDOM FOREST ALGORITHM

2020 ◽  
pp. 12-15
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Cancer Detection ◽  
Learning Algorithm ◽  
Breast Cancer Dataset ◽  
Random Forest Algorithm ◽  
Training Set ◽  
Cancer Dataset ◽  
Breast Cells

Breast Cancer is one of the most dangerous diseases for women. This cancer occurs when some breast cells begin to grow abnormally. Machine learning is the subfield of computer science that studies programs that generalize from past experience. This project looks at classification, where an algorithm tries to predict the label for a sample. The machine learning algorithm takes many of these samples, called the training set, and builds an internal model. This built model is used to classify and predict the data. There are two classes, benign and malignant. Random Forest classifier is used to predict whether the cancer is benign or malignant. Training and testing of the model are done by Wisconsin Diagnosis Breast Cancer dataset.

A Novel Breast Cancer Detection Technology Using an Advanced Transfer Maximal Entropy Clustering Algorithm

2019 ◽  
Vol 9 (8) ◽  
pp. 1639-1644
Author(s):  
Lifang Peng ◽  
Bin Huang ◽  
Kefu Chen ◽  
Leyuan Zhou
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Clustering Algorithm ◽  
Small Sample Size ◽  
Clustering Algorithms ◽  
Small Sample ◽  
Maximal Entropy ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Detection Technology

The initial diagnosis of breast cancer involves analyzing the relevant examination report of the patient to determine whether the tumor is benign or malignant. Unsupervised clustering algorithms can be used with this type of problem. In a cluster analysis of a patient's examination data, the clustering results and the preliminary diagnosis results are obtained. However, due to the high cost of detection, medical datasets often have a small sample size or lack information. The traditional clustering technique usually has poor clustering effects in such scenarios. To solve this problem, this paper proposes an advanced transfer learning mechanism based on the classic maximum entropy clustering algorithm and proposes an advanced transfer maximal entropy clustering (AT-MEC) algorithm. A simulation experiment using the Wisconsin Breast Cancer Dataset is performed. This paper verifies that the proposed AT-MEC algorithm has a better clustering effect than other clustering algorithms in the Wisconsin Breast Cancer Dataset.

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