Feature selection using Artificial Bee Colony algorithm for medical image classification

Agrawal, Vartika; Chandra, Satish

doi:10.1109/ic3.2015.7346674

Cited by 41 publications

(20 citation statements)

References 8 publications

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“…Several swarm intelligence algorithms have been used in the attributes selection [9][10][11][12][13][14][15][16][17]. Unfortunately, no single stable strategy exists to reduce the burden of computing and extracting highly correlated risk factors to the data and improve the classifier performance and achieve high accuracy.…”

Section: Bat Algorithm For Feature Selectionmentioning

confidence: 99%

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Sagban

Marhoon

Alubady

2020

IJECE

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Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.

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Section: Bat Algorithm For Feature Selectionmentioning

confidence: 99%

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Sagban

Marhoon

Alubady

2020

IJECE

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“…Zhang et al performed classification of magnetic resonance brain images (MRI) based on weighted Fractional Fourier Transform and non-parallel support vector machines [8]. Agrawal et al used artificial bee colony algorithm combining with k-nearest neighbor algorithm and support vector machine to classify 271 computed tomography (CT) images of cervical cancer [9]. The main disadvantage of traditional machine learning methods is the relatively poor robustness because of the limited capacity of processing large amounts of images.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Multiple Lesions on Chest X-ray Images: Classification Using a Neural Network Technique with Association-Specific Contexts

Guo

Zhang

et al. 2020

Applied Sciences

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Automated detection of lung lesions on Chest X-ray images shows good performance to reduce lung cancer mortality. However, it is difficult to detect multiple lesions of single image well and truly, and additional efforts are needed to improve diagnostic efficiency and quality. In this paper, a multi-label classification model combining attention-based neural networks and association-specific contexts is proposed for the detection of multiple lesions on chest X-ray images. A convolutional neural network and a long short-term memory network are first aligned by an attention mechanism to take advantage of both image and text information for the detection, called CNN-ATTENTION-LSTM (CAL) network. In addition, a mining method of implicit association strength to obtain an association network of chest lesions (CLA) network is designed to guide the training of CAL network. The CLA network provides possible clinical relationships between lesions to help the CAL network obtain better predictions. Experimental results on ChestX-ray14 dataset show that our method outperforms some state-of-the-art models under the metrics of area under curve (AUC), precision, recall, and F-score and achieves up to 85.4% in the case of atelectasis and infiltration. It indicates that the method may be useful in the computer-aided detection of multiple lesions on chest X-ray images.

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“…Further, the need for dimension reduction prior to classification with gene expression microarray approach has been outlined. Recently the swarm-based methods and evolutionary methods like Ant Colony Optimization (ACO) [8]- [10], Genetic Algorithm (GA) [11]- [13], Artificial Bee Colony (ABC) [14], [15] Particle Swarm Optimization (PSO) [16], [17] and Harmony Search Algorithm (HSA) have been used to handle the problems of features selection [18], [19]. Firefly algorithm (FA) was developed by Yang [20], [21] as a swarm-based metaheuristic.…”

Section: Introductionmentioning

confidence: 99%

A classification model on tumor cancer disease based mutual information and firefly algorithm

Jabar¹

2019

PEN

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Cancer is a globally recognized cause of death. A proper cancer analysis demands the classification of several types of tumor. Investigations into microarray gene expressions seem to be a successful platform for revising genetic diseases. Although the standard machine learning (ML) approaches have been efficient in the realization of significant genes and in the classification of new types of cancer cases, their medical and logical application has faced several drawbacks such as DNA microarray data analysis limitation, which includes an incredible number of features and the relatively small size of an instance. To achieve a reasonable and efficient DNA microarray dataset information, there is a need to extend the level of interpretability and forecast approach while maintaining a great level of precision. In this work, a novel way of cancer classification based on based gene expression profiles is presented. This method is a combination of both Firefly algorithm and Mutual Information Method. First, the features are used to select the features before using the Firefly algorithm for feature reduction. Finally, the Support Vector Machine is used to classify cancer into types. The performance of the proposed system was evaluated by using it to classify datasets from colon cancer; the results of the evaluation were compared with some recent approaches.

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Feature selection using Artificial Bee Colony algorithm for medical image classification

Cited by 41 publications

References 8 publications

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Automated Detection of Multiple Lesions on Chest X-ray Images: Classification Using a Neural Network Technique with Association-Specific Contexts

A classification model on tumor cancer disease based mutual information and firefly algorithm

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