Classification of abnormalities in breast ultrasound images using ANN, FIS and ANFIS classifier: A comparison

Precious, J. Glory; Selvan, S.; Avudaiammal, R.

doi:10.1088/1742-6596/1916/1/012015

Cited by 3 publications

(9 citation statements)

References 5 publications

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“…Four images were randomly selected in the DDSM data sets to compare the tumour classification features of breast ultrasound images found under various algorithms, as shown in Figure 4. Figure 4 shows the lesions area recognition results of the proposed algorithm, the AICA [9], the GNCA [10], the NMLF [11], the NNCA [12], and the NDLM [13]. In image 1 of Figure 4, the outline of GNCA [10] is relatively clear and complete, but the lesions recognition outline of the AICA [9], the NMLF [11], the NNCA [12] and NDLM [13] is relatively fuzzy.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 4 shows the lesions area recognition results of the proposed algorithm, the AICA [9], the GNCA [10], the NMLF [11], the NNCA [12], and the NDLM [13]. In image 1 of Figure 4, the outline of GNCA [10] is relatively clear and complete, but the lesions recognition outline of the AICA [9], the NMLF [11], the NNCA [12] and NDLM [13] is relatively fuzzy. In image 2 of Figure 4, the recognition contour of the five algorithms is relatively clear, but it is difficult to accurately recognize the image edge features, resulting in a decline in recognition effect.…”

Section: Resultsmentioning

confidence: 99%

“…According to the data in Figure 5, it can be seen that for DDSM data set, the SEN value of the classification of proposed algorithm is 85.6%, which is 21.1%, 23.9%, 3.6%, 32.4% and 23.3% higher than the algorithms in AICA [9], the GNCA [10], the NMLF [11], the NNCA [12], and the NDLM [13], respectively. For the INbreast data set, the SEN value of the classification of proposed algorithm is 89.7%, which is 2.1%, 6.2%, 9.7%, 8.4% and 10.5% higher than the algorithms in AICA [9], the GNCA [10], the NMLF [11], the NNCA [12], and the NDLM [13], respectively. The SEN of the proposed algorithm is the highest, which indicates that the accuracy of the quantitative feature classification for breast ultrasound images is high.…”

Section: Resultsmentioning

confidence: 99%

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Quantitative feature classification for breast ultrasound images using improved naive bayes

Sang

et al. 2022

IET Image Processing

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The quantitative feature classification of breast ultrasound images can explore the intrinsic connection between image features and lesions, and become an important basis for the diagnosis of tumor nature in clinical medicine. However, the tumor features extracted from breast ultrasound images are generally based on the overall situation, which has an impact on the classification of features in ultrasound images. Therefore, a quantitative feature classification algorithm for breast ultrasound images using improved Naive Bayes (NB) is proposed. First, histogram features, grayscale co‐generation matrix and other texture features are extracted from the ultrasound image. Second, NB is combined with decision tree calculation to improve the traditional NB algorithm. Finally, feature classification is optimized using improved NB algorithm to achieve high accuracy in the classification of ultrasound images. The results show that the contour of the lesion recognition results obtained by the proposed algorithm is the most complete, and the other regions can maximize the retention of the texture features of the original image. The classification accuracy is high, and the classification time of quantitative features of breast ultrasound images is short. It has application value in the field of breast ultrasound diagnosis.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Quantitative feature classification for breast ultrasound images using improved naive bayes

Sang

et al. 2022

IET Image Processing

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“…Reference [68] implemented ANFIS method to classify brain images and results in 99.6% accuracy. Reference [69] carried out a comparison review among ANN, FIS, and ANFIS models to identify the method with the highest accuracy. As a result, ANN, FIS, and ANFIS output 92.3, 88, and 96%, respectively.…”

Section: Deep Neural Networkmentioning

confidence: 99%

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Ravichandran

Keikhosrokiani

2022

Neural Comput & Applic

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The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, Taylor & Francis, Wiley, Google Scholar are used as databases to find relevant papers since 2018–2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification.

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“… 11 A better classification model is needed to classify the heartbeat sound, and therefore other field research is taken into consideration to select a better classifier. 12 In this research, a comparison is made of breast ultrasound classification. The study used ANN, FIS, and ANFIS, and the result shows that ANFIS has 96% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

et al. 2023

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Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.

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Classification of abnormalities in breast ultrasound images using ANN, FIS and ANFIS classifier: A comparison

Cited by 3 publications

References 5 publications

Quantitative feature classification for breast ultrasound images using improved naive bayes

Quantitative feature classification for breast ultrasound images using improved naive bayes

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

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