Mammogram classification using contourlet features with forest optimization-based feature selection approach

Mohanty, Figlu; Rup, Suvendu; Dash, Bodhisattva; Majhi, Banshidhar; Swamy, M.N.S.

doi:10.1007/s11042-018-5804-0

Cited by 51 publications

(32 citation statements)

References 28 publications

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“…The accuracies of the proposed method were better than that in Beura et al [15], which had an accuracy of 98% and 98.8% with MIAS and the digital database for screening mammography (DDSM) [8], respectively. The accuracies were greater than the methods in Pawar and Talbar [16] and Mohanty et al [17] which applied FS approaches to reach an accuracy of 89.47% and 97.86%. Moreover, the proposed CAD system has AUC of 1.0, 0.995, and 1.0 using the Adaboosting of J48 DT, wrapper k-NN based on random search and wrapper k-NN based on the best first search.…”

Section: Resultsmentioning

confidence: 65%

“…al. [17]. Moreover, when combining the two datasets together, the accuracies achieved using the Adaboosting of J48 DT, wrapper k-NN based on a random search and wrapper k-NN based on the best first search were 99.7%, 99.7%, and 100%.…”

Section: Resultsmentioning

confidence: 93%

“…Pawar and Talbar [16] proposed a genetic fuzzy FS approach which results in an accuracy of 89.47%. Mohanty et al [17] proposed an FS algorithm named forest optimization. The different classifiers such as SVM, k-nearest neighbor (k-NN), Naïve Bayes, and C4.5 were used to classify normal and abnormal mammogram lesions.…”

Section: Introductionmentioning

confidence: 99%

“…The highest accuracy achieved was 99.08 % for the C4.5 classifier using the DDSM dataset. The authors used the MIAS dataset as well and achieved an accuracy of 97.86% for the Naïve Bayes classifier [17].…”

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

2019

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Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100 %, which was achieved with k‐NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.

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Section: Resultsmentioning

confidence: 65%

Section: Resultsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

2019

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“…The paper [14] deals with one of the major health issues across the world which is the Breast Cancer. The authors propose a hybrid Computer Aided Diagnosisframework to classify suspicious regions into normal or abnormal, and further, benign or malignant.…”

mentioning

confidence: 99%

Guest Editorial: Advances in Computational Intelligence for Multimodal Biomedical Imaging

Jennane

Hamida

Benali

et al. 2019

Multimed Tools Appl

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Nowadays, many modalities such as CT, X-ray scanners, MRI/fMRI, PET scan, etc. generate complex images with a large amount of data that are becoming extremely difficult to handle. This growing mass of data requires new strategies for the diagnosis of diseases and new therapies. In recent years, particular attention has been paid to computational intelligence methods in multimodal biomedical imaging applications. Inspired by artificial intelligence, mathematics, biology and other fields, these methods can find relationships between different categories of this complex data and provide a set of tools for the diagnosis and monitoring of the disease. This special issue provides a forum to publish original research papers covering the state-ofthe-art, new algorithms, methodologies, theories and implementations of computational intelligence methods for computer-aided diagnostic systems and multimodal biomedical imaging applications such as classification, restoration and registration.

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A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Sakthivel¹,

Subasree

Malik

et al. 2022

Concurrency and Computation

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In this manuscript, a Wrapper based feature extraction framework based on AlexNet deep convolutional neural network (ADCNN) with gradient‐based optimizer (GBO) is proposed for early detection of breast cancer. In this input images are occupied as mini‐mammography image analysis society (MIAS) database. Then the images are pre‐processed to eliminate that noises using Markov random field (MRF) method. Image features are extracted by the process of Wrapper based feature extraction framework with ADCNN. Then, the weight parameters of ADCNN are optimizing through the aid of GBO. Then the mammogram images are characterized as normal or abnormal (malignant and benign) with SVM classifier. The simulation process is implemented on MATLAB platform. The proposed ADCNN‐SVM‐GBO attains higher accuracy 34.64%, 28.86%, 19.86%, 24.64%, 32.86%, higher Precision 28.07%, 18.96%, 16.86%, 25.86%, 26.86%, higher recall 27.86%, 32.54%, 27.86%, 23.95%, 19.97%, and the efficiency of the proposed method FE‐ADCNN‐GBO‐SVM is likened with the existing processes. Classification of mammograms depends features removal processes with support vector machine (FE‐LBP‐GLCM‐SVM), breast cancer classification with global discriminate features on mammographic images (FE‐GLCM‐ANN), enhancing breast cancer classification with (SMOTE) technique and pectoral muscle removal on mammographic images (FE‐SMOTE‐RF), application of artificial intelligence depends deep learning on breast cancer screening and imaging diagnosis (FE‐CNN‐CDCNN) respectively.

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Mammogram classification using contourlet features with forest optimization-based feature selection approach

Cited by 51 publications

References 28 publications

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

Guest Editorial: Advances in Computational Intelligence for Multimodal Biomedical Imaging

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

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