Early detection of breast cancer using optimized ANFIS and features selection

Addeh, Jalil; Demirel, Hasan; Zarbakhsh, Payam

doi:10.1109/cicn.2017.8319352

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…According to the literature, although ANFIS has provided promising results in brain cancer detection (Chatterjee & Das, 2019; Selvapandian & Manivannam, 2018; Thirumurugan & Shanthakumar, 2016) and in mammography‐based breast cancer detection (Addeh et al, 2018; Padmavathy et al, 2018; Sujatha et al, 2020) it has never been applied to breast DCE‐MRI data.…”

Section: Resultsmentioning

confidence: 99%

“…Concerning breast tumour diagnosis (Hosseini & Zekri, 2012) ANFIS was used in several studies combined with different feature extraction techniques. In particular, an ANFIS feature selection method was proposed for breast cancer detection (Addeh et al, 2018) based on the row data of Breast Cancer Wisconsin (WBCD) dataset. Additionally, ANFIS was used for breast cancer detection in mammographic images that are non‐subsampled shearlet transform (NSST) pre‐processed (Padmavathy et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

et al. 2021

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The authors propose a method for breast dynamic contrast enhanced‐magnetic resonance imaging classification by combining radiomic texture analysis with a hybrid adaptive neuro‐fuzzy inference system (ANFIS)‐particle swarm optimization (PSO) classifier. The fast discrete curvelet transform is utilized as a decomposition scheme in multiple scales. The mean and entropy features extracted from the produced scheme are used as texture descriptors. Principal component analysis (PCA) involves reduction of the dimensionality of the initial feature set. The transformed feature vector is subsequently introduced to a hybrid ANFIS‐PSO classifier. The average overall classification power of the proposed hybrid ANFIS‐PSO classifier is comparatively assessed to that obtained using several classifiers (ANFIS, linear discriminant analysis, Naïve Bayes, artificial neural networks, random forest and support vector machine) by using the 70 training‐30 testing data ratio. The comparison performed highlights the superiority of the proposed methodology, thus underlying the potential of ANFIS‐PSO for the breast cancer diagnosis with a classification accuracy of 94%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

et al. 2021

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“…it is also considered as the second most common cancer worldwide (Dhungel, Carneiro & Bradley, 2015). Early detection and accurate diagnosis of breast cancer can tremendously contribute to the reduction of fatality rate and remarkably important for the reduction of its morbidity and mortality (Addeh, Demirel & Zarbakhsh, 2017;Moodley et al, 2018). A cost-effective computeraided detection/diagnosis technique can play a crucial part in reducing interpretation error and provide an automated diagnosis of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Diagnosis Using Wrapper-Based Feature Selection and Artificial Neural Network

Naveed

MADHLOOM

HUSAIN

2021

acs

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Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role in reducing the fatality rate. The main objective of this study is to propose an efficient approach to classify breast cancer tumor into either benign or malignant based on digitized image of a fine needle aspirate (FNA) of a breast mass represented by the Wisconsin Breast Cancer Dataset. Two wrapper-based feature selection methods, namely, sequential forward selection(SFS) and sequential backward selection (SBS) are used to identify the most discriminant features which can contribute to improve the classification performance. The feed forward neural network (FFNN) is used as a classification algorithm. The learning algorithm hyper-parameters are optimized using the grid search process. After selecting the optimal classification model, the data is divided into training set and testing set and the performance was evaluated. The feature space is reduced from nine feature to seven and six features using SFS and SBS respectively. The highest classification accuracy recorded was 99.03% with FFNN using the seven SFS selected features. While accuracy recorded with the six SBS selected features was 98.54%. The obtained results indicate that the proposed approach is effective in terms of feature space reduction leading to better accuracy and efficient classification model.

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“…Expert systems in health have been growing lately, one of which is the Adaptive Neuro Fuzzy Inference System (ANFIS) [16]. High detection accuracy for breast cancer early detection was obtained using ANFIS [17]. Lung cancer classification using ANFIS gives better performance compared with Fuzzy Inference System (FIS) [18].…”

Section: Introductionmentioning

confidence: 99%

An application of ANFIS for Lung Diseases Early Detection System

Santoso

Disrinama

Amrullah

2020

KINETIK

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Indonesian Basic Health Research in 2018 showed the prevalence of pneumonia, pulmonary tuberculosis (TB) and lung cancer in Indonesia 4.0% 0.4% and 0.18%, respectively. However, the number of lung specialists is small. According to the Indonesian Lung Specialist Association webpage, the number of doctors joined in the association up to 2008 were 452. This amount is very less when compared with existing lung disease cases. Thus, the handling of lung disease will be too late. The use of ANFIS for early detection of lung disease is growing. However, the systems designed are need preprocessing data to be executed and still applied for one type of disease. This paper will design a desktop application based on ANFIS expert system to detect lung disease early, i.e. for pneumonia, pulmonary TB and lung cancer. The system will work based on simple symptoms expressed by the patient. Subtractive clustering is used for clustering process. The results of the training showed that the models were able to give better performance compared to the model built using conventional clustering methods. The test results show that those three models have comparable performance compared to their counterpart. Software validation shows that the it gives 94.00% succeed for training data and up to 100% for testing data. This application is not intended to replace the role of a doctor, but to help diagnose the patient's condition earlier.

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Early detection of breast cancer using optimized ANFIS and features selection

Cited by 12 publications

References 18 publications

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

Breast Cancer Diagnosis Using Wrapper-Based Feature Selection and Artificial Neural Network

An application of ANFIS for Lung Diseases Early Detection System

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