scholarly journals A Review on Feature Selection Techniques in Digital Mammograms

2021 ◽  
Vol 12 (2) ◽  
pp. 3329-3338
Author(s):  
L Kanya kumara, Et. al.
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Accurate Method ◽  
Optimization Techniques ◽  
Computer Aided Detection ◽  
Resonance Imaging ◽  
Cad Systems ◽  
The World ◽  
Feature Selection Techniques ◽  
Low Dosage

The most of the women in the world are suffering from a deadly disease called Breast Cancer (BC). Breast cancer is analyzed by using imaging modalities such as mammograms, magnetic resonance imaging, ultrasound, and thermograms. Among all, mammograms are the low dosage, less cost, more effective, and accurate method to detect BC in early stages. There are many Computer-Aided Detection (CAD) systems for the automatic detection of masses in mammograms. These techniques are helping radiologists and physicians in diagnosing disease. The objective of this paper is to overview different CAD systems in which mainly we focused on feature selection, as feature selection techniques are used to reduce the complexity of the classifiers and also increase the accuracy. We conclude that suitable optimization techniques should be chosen to increase the accuracy of the classifier so that we can increase the survival rate of the patient.

BREAST CANCER DETECTION USING RSFS-BASED FEATURE SELECTION ALGORITHMS IN THERMAL IMAGES

2021 ◽  
pp. 2150020
Author(s):  
Nazila Darabi ◽  
Abdalhossein Rezai ◽  
Seyedeh Shahrbanoo Falahieh Hamidpour
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cancer Detection ◽  
Vital Role ◽  
Support Vector ◽  
Computer Aided Detection ◽  
K Nearest Neighbors ◽  
Cad System ◽  
Common Cancer ◽  
Selection Algorithms

Breast cancer is a common cancer in female. Accurate and early detection of breast cancer can play a vital role in treatment. This paper presents and evaluates a thermogram based Computer-Aided Detection (CAD) system for the detection of breast cancer. In this CAD system, the Random Subset Feature Selection (RSFS) algorithm and hybrid of minimum Redundancy Maximum Relevance (mRMR) algorithm and Genetic Algorithm (GA) with RSFS algorithm are utilized for feature selection. In addition, the Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) algorithms are utilized as classifier algorithm. The proposed CAD system is verified using MATLAB 2017 and a dataset that is composed of breast images from 78 patients. The implementation results demonstrate that using RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 85.36% and 75%, and sensitivity of 94.11% and 79.31%, respectively. In addition, using hybrid GA and RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 83.87% and 69.56%, and sensitivity of 96% and 81.81%, respectively, and using hybrid mRMR and RSFS algorithms for feature selection and kNN and SVM algorithms as classifier have accuracy of 77.41% and 73.07%, and sensitivity of 98% and 72.72%, respectively.

scholarly journals An Effective Classification Algorithm for Breast Cancer using Dyadic Projection

2020 ◽  
Vol 9 (1) ◽  
pp. 2323-2327
Keyword(s):  
Breast Cancer ◽  
Breast Tumors ◽  
Wide Spread ◽  
Cad Systems ◽  
Proposed Model ◽  
The World ◽  
Diagnosis Method ◽  
Feature Acquisition ◽  
Working Out ◽  
Aided Diagnosis

Breast Cancer is a wide spread reason for the deaths of women in the world. Nowadays, CAD systems have become the most increasing interest in its detection. In this paper, a new computer-aided diagnosis method is introduced to help oncologists to classify it as benign or evil breast tumors in ultrasound. In the proposed model, bi clustering is done for feature acquisition and then dyadic transform is applied. Biclustering mining is used as a key to identify the regularity patterns in columns on the working out data, Biclustering mining is utilized as a key. At last, to identify the perfect combinations and put them into a strong classifier, AdaBoost learning is applied. Using a dataset the proposed method is evaluated validated and the results are compared with the results of existing methods. The results of the proposed model showed the best calculation, proving it to be effective in laboratory applications

scholarly journals Research of Feature Selection Methods to Predict Breast Cancer

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2353-2355 ◽  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Early Stage ◽  
Classification Methods ◽  
Selection Methods ◽  
Breast Cancer Data ◽  
Cancer Data ◽  
Cancer Breast ◽  
Data Mining Approach ◽  
The World

Human health is most important than anything in the world, one should take care of it. Among various disease, cancer is the most terrible and deadly disease, so it is necessary to predict such disease in early stage. In this paper different feature selection methods used for feature extraction with different feature classification methods to identify the breast cancer. Breast cancer data is taken from UCI repository and is processed using WEKA tool and proposed techniques are applied to classify data accurately. This study well defines that data mining approach is suitable for predicting breast cancer.

scholarly journals Detection of Breast Cancer Through Clinical Data Using Supervised and Unsupervised Feature Selection Techniques

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 22090-22105
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Abdus Saboor ◽  
Jalaluddin Khan ◽  
Samad Wali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Clinical Data ◽  
Unsupervised Feature Selection ◽  
Feature Selection Techniques

scholarly journals A Research on Computer Aided Detection System for Women Breast Cancer Diagnosis from Digital Mammographic Images

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1008-1014
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Death Rate ◽  
Computational Cost ◽  
Breast Cancer Diagnosis ◽  
Computer Aided Detection ◽  
Cad Systems ◽  
Cad System ◽  
Mammographic Images ◽  
Computer Aided

The Women breast cancer is the most critical cancer that are found in women. Its the second important cause of death in the world. Breast cancer has been ranked number one cancer in Indian females with rates occurrence of 25.8 per 1,00,000 females and death rate 12.7 among 1,00,000. Generally breast cancer is a malignant tumor that begins in the cells of the breast and eventually it spreads to the surrounding tissues. Early detection and diagnosis can reduce the mortality rate. Radiologist misdiagnosis the disease due to technical issues such as imaging quality and human error. Radiologists can improve the performance of Computer Aided Detection/Diagnosis (CAD) systems to finding and discriminating between the normal and abnormal tissues. Breast cancer diagnosis can applied are applied recent CAD systems on imaging modalities such as mammogram, ultrasound, MRI and biopsy histopathological images. CAD system have four stages for diagnosis which are pre-processing, segmentation, Feature Extraction and Classification. CAD system are developed to reduce the time taken to diagnose the breast cancer and reduce the death rate. This paper focus on the survey of CAD system to detect women breast cancer disease from the digital mammographic images to achieve high accuracy and low computational cost.

Efficiency and Scalability Methods in Cancer Detection Problems

2013 ◽  
pp. 75-94
Author(s):  
Inna Stainvas ◽  
Alexandra Manevitch
Keyword(s):  
Feature Selection ◽  
Cancer Detection ◽  
Learning Algorithms ◽  
Computer Aided Detection ◽  
Large Dataset ◽  
Cad Systems ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
New Challenges

Computer aided detection (CAD) system for cancer detection from X-ray images is highly requested by radiologists. For CAD systems to be successful, a large amount of data has to be collected. This poses new challenges for developing learning algorithms that are efficient and scalable to large dataset sizes. One way to achieve this efficiency is by using good feature selection.

scholarly journals An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sunil Kumar Prabhakar ◽  
Harikumar Rajaguru ◽  
Sun-Hee Kim
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Feature Selection ◽  
Fruit Fly ◽  
Optimization Techniques ◽  
Microarray Technology ◽  
Invasive Weed ◽  
Fruit Fly Optimization ◽  
Search Optimization ◽  
Feature Selection Techniques

One of the deadliest diseases which affects the large intestine is colon cancer. Older adults are typically affected by colon cancer though it can happen at any age. It generally starts as small benign growth of cells that forms on the inside of the colon, and later, it develops into cancer. Due to the propagation of somatic alterations that affects the gene expression, colon cancer is caused. A standardized format for assessing the expression levels of thousands of genes is provided by the DNA microarray technology. The tumors of various anatomical regions can be distinguished by the patterns of gene expression in microarray technology. As the microarray data is too huge to process due to the curse of dimensionality problem, an amalgamated approach of utilizing bilevel feature selection techniques is proposed in this paper. In the first level, the genes or the features are dimensionally reduced with the help of Multivariate Minimum Redundancy–Maximum Relevance (MRMR) technique. Then, in the second level, six optimization techniques are utilized in this work for selecting the best genes or features before proceeding to classification process. The optimization techniques considered in this work are Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), League Championship Optimization (LCO), Beetle Antennae Search Optimization (BASO), Crow Search Optimization (CSO), and Fruit Fly Optimization (FFO). Finally, it is classified with five suitable classifiers, and the best results show when IWO is utilized with MRMR, and then classified with Quadratic Discriminant Analysis (QDA), a classification accuracy of 99.16% is obtained.

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