Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier

Rajendran, Reenadevi; Balasubramaniam, Sathiyabhama; Ravi, Vinayakumar; Sankar, S.

doi:10.1111/coin.12522

Cited by 14 publications

(11 citation statements)

References 55 publications

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“…Rajendran [20] implemented the hybrid optimization approach which was the combination of the grasshopper optimization algorithm (GOA) and crow search algorithm (CSA) for eliminating the optimal features. Here, the multilayer perceptron (MLP) neural network was used to perform the classification.…”

Section: Related Workmentioning

confidence: 99%

Effective Feature Selection Using Multi-Objective Improved Ant Colony Optimization for Breast Cancer Classification

2023

IJIES

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Breast cancer is considered one of the main reasons for the mortality of females around the world, therefore the recognition and classification of the initial stage of breast cancer are essential to help the patients to perform the required action. However, the precise classification of breast cancer is a difficult task in medical imaging because of the complexity of breast tissues. In this paper, Otsu's thresholding based segmentation (OTS) is used for segmenting the cancer regions from the images of mammographic image analysis society (MIAS) dataset. Subsequently, feature extraction using Alexnet is done for extracting the features. The feature selection using multi-objective improved ant colony optimization (MIACO) is proposed in this paper to select the optimal features from the overall features acquired using Alexnet. The MIACO is mainly selects the features based on correlation coefficient to choose the features with higher correlation. Further, the stacked autoencoder (SAE) is used to perform an effective breast cancer classification using the features selected by MIACO. The performance of MIACO-SAE method is analyzed by means of accuracy, precision, recall, characteristic stability index (CSI) and F-measure. The existing research namely threshold-based and trainable segmentation (TTS) with machine learning (ML), infinite feature selection with genetic algorithm (IFS-GA) and deep convolutional neural networks (DCNN) are used to evaluate the MIACO-SAE method. The classification accuracy of MIACO-SAE is 99.36%, which is high when compared to the TTS-ML, IFS-GA and DCNN.

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Section: Related Workmentioning

confidence: 99%

Effective Feature Selection Using Multi-Objective Improved Ant Colony Optimization for Breast Cancer Classification

2023

IJIES

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“…The performance of the new approach was investigated using CIC2017, NSL-KDD, BoT-IoT, and KDD99 datasets. In 25 , Grasshopper Optimization Algorithm and the Crow Search Algorithm were hybridized to address the challenge of feature selection leading to classification using MLP. Results showed that when combined with MLP, the hybrid method returned the values of 97.1%, 98%, and 95.4% for accuracy, sensitivity and specificity, respectively, using a mammographic dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Even the few studies which have considered this challenge have only applied methods using peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT), singular value decomposition (SVD), staked CNN architectures, dual-stream deep architecture, dimensionality reduction like principal component analysis (PCA), feature fusion which improves features resulting from feature extraction using deep convolutional neural network (DCNN), and CNN-based auto features extraction (CNN-AFE). For example, Sahlol et al 23 applied Salp Swarm Algorithm (SESSA) to improve the selection of preferred detected features leading to the classification of White Blood Cell (WBC) Leukaemia in samples; Fatani et al 24 used the traditional method of CNN for extracting features, and then applied Aquila Optimizer (AQU) for further feature selection; in 25 , the Grasshopper Optimization Algorithm and the Crow Search Algorithm were hybridized to address the challenge of feature selection leading to classification using MLP. Whale Optimization Algorithm (WOA), which was hybridized with Flower Pollination Algorithm (FPA), was investigated in 26 for feature selection for email.…”

Section: Introductionmentioning

confidence: 99%

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

Oyelade

Ezugwu

2022

Sci Rep

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Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature extraction and classification has been widely reported in the literature to have achieved outstanding performance and acceptance in the disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by the convolutional operations are passed on to the classifier, to avoid bottlenecking the classification operation. Unfortunately, since this has been left unaddressed, a subtle performance impairment has resulted from this omission. Therefore, this study is devoted to addressing these drawbacks using a metaheuristic algorithm to optimize the number of features extracted by the CNN, so that suggestive features are applied for the classification process. To achieve this, a new variant of the Ebola-based optimization algorithm is proposed, based on the population immunity concept and the use of a chaos mapping initialization strategy. The resulting algorithm, called the immunity-based Ebola optimization search algorithm (IEOSA), is applied to the optimization problem addressed in the study. The optimized features represent the output from the IEOSA, which receives the noisy and unfiltered detected features from the convolutional process as input. An exhaustive evaluation of the IEOSA was carried out using classical and IEEE CEC benchmarked functions. A comparative analysis of the performance of IEOSA is presented, with some recent optimization algorithms. The experimental result showed that IEOSA performed well on all the tested benchmark functions. Furthermore, IEOSA was then applied to solve the feature enhancement and selection problem in CNN for better prediction of breast cancer in digital mammography. The classification accuracy returned by the IEOSA method showed that the new approach improved the classification process on detected features when using CNN models.

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“…e work presented by [30] proposes an automated breast cancer diagnosing system by employing the GVFSnake Segmentation method, the wavelet-based feature extraction, and the fuzzy-based classification. A hybrid optimization algorithm-based feature selection for mammogram images and hybrid transfer learning for detecting the breast masses of mammographs are presented by [31,32]. e authors in [33] proposed a novel computer-aided diagnosis (CAD) system based on one of the regional deep learning techniques, an ROI-based convolutional neural network for simultaneous detection and classification of breast masses in digital mammograms.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques

Al-Fahaidy

Al-Fuhaidi

AL-Darouby

et al. 2022

Applied Computational Intelligence and Soft Computing

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Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature’s extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%.

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Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier

Cited by 14 publications

References 55 publications

Effective Feature Selection Using Multi-Objective Improved Ant Colony Optimization for Breast Cancer Classification

Effective Feature Selection Using Multi-Objective Improved Ant Colony Optimization for Breast Cancer Classification

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques

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