Screening and early identification of microcalcifications in breast using texture-based ANFIS classification

Sujatha, K.; Durgadevi, G.; Kumar, K. Senthil; Karthikeyan, V.; Ponmagal, R.S.; Hari, Rajeswari; Bhavani, N. P. G.; Srividhya, V.; Cao, Su-Qun

doi:10.1016/b978-0-12-815369-7.00005-7

Cited by 7 publications

(4 citation statements)

References 17 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%

“…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). In breast ultrasound images, ANFIS was proposed breast cancer detection (Huang et al, 2012).…”

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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“…This way, the users can take better decisions according to the risk and uncertainty of the system. This is why this method is referred (Das et al 2020) 2020 Medical disease analysis Feature Extraction Model using Neuro-Fuzzy for classification (Tiwari et al 2020) 2020 Lung Cancer Fuzzy Inference System for detection of lung cancer (Kour et al 2020) 2020 Medical disease analysis Neuro-fuzzy systems for prediction and classification of different types of diseases (Vidhya & Shanmugalakshmi, 2020) 2020 Medical disease analysis Modified-ANFIS using various disease analysis based on medical Big Data (Ranjit et al 2020) 2020 Knee Diseases Knee Diseases Prediction using adaptive and improved ANFIS (Hekmat et al 2020) 2020 Acute kidney Injury Risk factors, prevalence, and early outcome analysis of acute kidney injury (Sood et al 2020) 2020 dengue fever LDA-ANFIS based dengue fever risk assessment framework (Sujatha et al 2020) 2020 Breast cancer Micro calcifications in breast identification utilizing ANFIS (Liu et al 2019) 2019 Prostate Cancer Using a fuzzy inference system, prostate cancer was predicted (Turabieh et al 2019) 2019 Breast cancer A D-ANFIS is used to handle the missing values in the application used for the Internet of Medical Things (Mori et al 2019) 2019 Medical decision making Extracting the relationship between input and output of the learning data using fuzzy rules (de Medeiros et al 2017) 2017 Medical decision making Real-time medical diagnosis using a fuzzy inference system (Nguyen et al 2015) 2015 Breast cancer A new classifier based on the type-2 fuzzy logic system for breast cancer diagnosis (Azar & Hassanien, 2015) 2014 Breast cancer Medical big data dimensionality reduction using a neuro-fuzzy classifier (Papageorgio...…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

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Screening and early identification of microcalcifications in breast using texture-based ANFIS classification

Cited by 7 publications

References 17 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

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

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