Adaptive clustering based breast cancer detection with ANFIS classifier using mammographic images

Padmavathy, T. V.; Vimalkumar, M. N.; Bhargava, D. S.

doi:10.1007/s10586-018-2160-9

Cited by 15 publications

(7 citation statements)

References 21 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%

“…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). Furthermore, it has been used as a classifier in mammograms based on texture features (Fernandes et al, 2010; Sujatha et al, 2020), but also based on the combination of shape and textural features (Bhattacharya & Das, 2009 Bhattacharya & Das, 2010) and also based on features extracted by a genetic algorithm (Das & Bhattacharya, 2011).…”

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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“…The workflow of the proposed method bone cancer detection 15 the accuracy was 93%. Breast cancer detection 16 , 17 , 18 , 19 , the detection was never better and the ANFIS classifier achieved an accuracy of the range 91%-99% . Finally, in chest diseases detection 20 , 21 ,the performance accuracy was around 98%.…”

Section: Literature Reviewmentioning

confidence: 98%

ANFIS-Net for Automatic Detection of COVID-19

Al-Ali

Elharrouss

Qidwai

et al. 2021

Preprint

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Among the most leading causes of mortality across the globe is infectious diseases. Human Immunodeficiency Virus infection and Acquired Immune Deficiency Syndrome (HIV/AIDS), H1N1 influenza and Poliomyelitis, Severe Acute Respiratory Syndrome (SARS) are all infectious diseases that has cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control, has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As Fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic detection of chest X-ray Images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. The results were promising, reaching 98.67% accuracy compared with the other state-of-the-art techniques.

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“…On the other hand, Suresh et al [ 49 ] and Sapate et al [ 50 ] employ a fuzzy-based strategy to cluster all the pixels with similar features in order to detect all the zones that have differences. Other strategies involve the utilization of mathematical morphology [ 51 , 52 , 53 , 54 , 55 ], image contrast and intensity [ 56 , 57 ], geometrical features [ 58 , 59 ], correlation and convolution [ 60 , 61 ], non-linear filtering [ 62 , 63 ], texture features [ 64 ], deep learning [ 65 , 66 , 67 , 68 , 69 ], entropy [ 70 , 71 ], among other strategies. It is worth noticing that from the diversity of the employed strategies, some of them still require an initial guidance to detect the suspicious zones, either by manually selecting pixels inside of the zone or using the radiologist notes about the localization.…”

Section: Image Processing and Classification Strategiesmentioning

confidence: 99%

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Basurto-Hurtado

Cruz-Albarran

Toledano‐Ayala

et al. 2022

Cancers

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Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

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Adaptive clustering based breast cancer detection with ANFIS classifier using mammographic images

Cited by 15 publications

References 21 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

ANFIS-Net for Automatic Detection of COVID-19

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

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