Mammogram Classification Using Curvelet GLCM Texture Features and GIST Features

Gardezi, Syed Jamal Safdar; Faye, Ibrahima; Adjed, Faouzi; Kamel, Nidal; Eltoukhy, Mohamed Meselhy

doi:10.1007/978-3-319-48308-5_67

Cited by 5 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Anyhow we already found a high enough accuracy, as close to 100% (98.8327%) as possible, with the EbkNN method. This accuracy is the best from amo n g st t h e wo rks o f other researchers on WDBC dataset [35,36,37,38,39,40,41,42,43] that the authors of this paper kno w o f. Th e wo rk b y [43] is the closest that it gets to our work. Their best accuracy is 98.62% for kNN with chi-square based feature selection.…”

Section: Discussionmentioning

confidence: 56%

Entropy Based k Nearest Neighbor Pattern Classification (EbkNN): En-route to Achieving a High Accuracy in Breast Cancer Diagnosis

Sinha

Sinha²

2020

AJAS

View full text Add to dashboard Cite

Entropy based k-Nearest Neighbor pattern classification (EbkNN) is a variation of the conventional k-Nearest Neighbor rule of pattern classification, which exclusively optimizes the value of k-neighbors for each test data based on the calculations of entropy. The formula for entropy used in EbkNN is the one that has been defined popularly in information theory for a set of n different types of information (class) attached to a total of m objects (data points) with each object defined by f features. In EbkNN that value of k is chosen for discrimination of given test data for which the entropy is the least non-zero value. Other rules of conventional kNN are retained in EbkNN. It is concluded that EbkNN works best for binary classification. It is computationally prohibitive to use EbkNN for discriminating the data points of the test dataset into number of classes greater than two. The biggest advantage of EbkNN vis-à-vis the conventional kNN is that in one single run of EbkNN algorithm we get optimum classification of test data. But conventional kNN algorithm has to be run separately for each of the selected range of values of k, and then the optimum k to be chosen from amongst them. We also tested our EbkNN method on WDBC (Wisconsin Diagnostic Breast Cancer) dataset. There are 569 instances in this dataset and we made a random choice of first 290 instances as training dataset and the rest 279 instances as test dataset. We got an exceptionally remarkable result with EbkNN method- accuracy close to 100% and better than the ones got by most of the other researchers who worked on WDBC dataset.

show abstract

Section: Discussionmentioning

confidence: 56%

Entropy Based k Nearest Neighbor Pattern Classification (EbkNN): En-route to Achieving a High Accuracy in Breast Cancer Diagnosis

Sinha

Sinha²

2020

AJAS

View full text Add to dashboard Cite

show abstract

“…Wavelet coefficient features with genetic fuzzy system. 89.47 Gardezi et al, (2016) Curvelet based grey level cooccurrence matrix and geometric invariant shift transform with support vector machine classifier.…”

Section: Resultsmentioning

confidence: 99%

Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Chakravarthy¹,

Rajaguru²

2019

Asian Pac J Cancer Prev

View full text Add to dashboard Cite

Objective:Breast cancer is the most common invasive severity which leads to the second primary cause of death among women. The objective of this paper is to propose a computer-aided approach for the breast cancer classification from the digital mammograms.Methods:Designing an effective classification approach will assist in resolving the difficulties in analyzing digital mammograms. The proposed work utilized the Mammogram Image Analysis Society (MIAS) database for the analysis of breast cancer. Five distinct wavelet families are used for extraction of features from the mammograms of MIAS database. These extracted features are statistical in nature and served as input to the Linear Discriminant Analysis (LDA) and Cuckoo-Search Algorithm (CSA) classifiers. Results:Error rate, Sensitivity, Specificity and Accuracy are the performance measures used and the obtained results clearly state that the CSA used as a classifier affords an accuracy of 97.5% while compared with the LDA classifier. Conclusion:The results of comparative performance analysis show that the CSA classifier outperforms the performance of LDA in terms of breast cancer classification.

show abstract

“…Classification There are varies classifiers from simple to complex that has been under use in most research papers. Random Forest (RF) [31], K-Nearest Neighbor (KNN), Support Vector Machine (SVM) [13,24,26], Multilayer Perceptron (MLP) and Naive Bayes (NB) [32] are some among those classifiers. The SVM classifier is used with sigmoid kernel function.…”

Section: Cnn Based Extracted Featuresmentioning

confidence: 99%

Classification of Mammograms Using Texture and CNN Based Extracted Features

Debelee

Gebreselasie

Schwenker³

et al. 2019

JBBBE

View full text Add to dashboard Cite

In this paper, a modified adaptive K-means (MAKM) method is proposed to extract the region of interest (ROI) from the local and public datasets. The local image datasets are collected from Bethezata General Hospital (BGH) and the public datasets are from Mammographic Image Analysis Society (MIAS). The same image number is used for both datasets, 112 are abnormal and 208 are normal. Two texture features (GLCM and Gabor) from ROIs and one CNN based extracted features are considered in the experiment. CNN features are extracted using Inception-V3 pre-trained model after simple preprocessing and cropping. The quality of the features are evaluated individually and by fusing features to one another and five classifiers (SVM, KNN, MLP, RF, and NB) are used to measure the descriptive power of the features using cross-validation. The proposed approach was first evaluated on the local dataset and then applied to the public dataset. The results of the classifiers are measured using accuracy, sensitivity, specificity, kappa, computation time and AUC. The experimental analysis made using GLCM features from the two datasets indicates that GLCM features from BGH dataset outperformed that of MIAS dataset in all five classifiers. However, Gabor features from the two datasets scored the best result with two classifiers (SVM and MLP). For BGH and MIAS, SVM scored an accuracy of 99%, 97.46%, the sensitivity of 99.48%, 96.26% and specificity of 98.16%, 100% respectively. And MLP achieved an accuracy of 97%, 87.64%, the sensitivity of 97.40%, 96.65% and specificity of 96.26%, 75.73% respectively. Relatively maximum performance is achieved for feature fusion between Gabor and CNN based extracted features using MLP classifier. However, KNN, MLP, RF, and NB classifiers achieved almost 100% performance for GLCM texture features and SVM scored an accuracy of 96.88%, the sensitivity of 97.14% and specificity of 96.36%. As compared to other classifiers, NB has scored the least computation time in all experiments.

show abstract

Mammogram Classification Using Curvelet GLCM Texture Features and GIST Features

Cited by 5 publications

References 16 publications

Entropy Based k Nearest Neighbor Pattern Classification (EbkNN): En-route to Achieving a High Accuracy in Breast Cancer Diagnosis

Entropy Based k Nearest Neighbor Pattern Classification (EbkNN): En-route to Achieving a High Accuracy in Breast Cancer Diagnosis

Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Classification of Mammograms Using Texture and CNN Based Extracted Features

Contact Info

Product

Resources

About