Hybrid Segmentation and Feature Extraction Approach to Detect Tumour Based on Fuzzy Rough-in Mammogram Images

Punitha, M; Perumal, K.

doi:10.1016/j.procs.2020.01.009

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…The diagnosis system of artificial intelligence in medicine has increased in recent years, which may be seen in the number of published articles [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. The KM++ based on the CSO method for mammogram evaluation used within this work presented high sensitivity, specificity, and accuracy for the segmentation of breast cancer.…”

Section: Discussionmentioning

confidence: 99%

“…A mammogram image used in this study is drawn from different datasets, some of which are from the Mini–MIAS [ 5 , 9 , 15 , 16 , 17 , 18 , 19 ] and DDSM [ 12 ], while others are from BCDR [ 10 ]. We used the same images to evaluate their detection methods [ 3 , 4 , 5 , 6 , 9 , 10 , 12 ]. This dataset from DDSM, Mini–MIAS, and BCDR comprises 2620, 323, and 123 images, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…BCDR contains 18 normal and 105 abnormal images from 123 images. Before discussing the mammogram image, it may be helpful to describe the structure of the image; see Figure 2 [ 5 , 9 , 10 , 12 , 15 , 16 , 17 , 18 , 19 ]. Figure 2 a is an image from Mini–MIAS, Figure 2 b is an image from INbreast, and Figure 2 c is an image from BCDR, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…BCDR contains 18 normal and 105 abnormal images from 123 im ages. Before discussing the mammogram image, it may be helpful to describe the structu of the image; see Figure 2 [5,9,10,12,15,17,18]. Figure 2a…”

Section: Dataset and Image Acquisitionmentioning

confidence: 99%

“…Different machine learning, including K–Nearest Neighbourhood (KNN), Fuzzy Clustering (FC), Neural Network (NN), Logistic Regression (LG), SVM, and Random Forest (RF), were applied for breast cancer detection. A similarity of three unsupervised learning, including Fuzzy Rough Segmentation (FRS), Rough Segmentation (RS), and Fuzzy Segmentation (FS), was proposed by Punitha et al [ 9 ]. Zebari et al [ 10 ] offered a thresholding method and binary morphological segmentation for breast cancer.…”

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Wisaeng

2022

Diagnostics

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Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high–quality features, which can result in very long processing times. Therefore, this paper proposes a new K–means++ clustering based on Cuckoo Search Optimization (KM++CSO) for breast cancer detection. The pre-processing method is used to improve the proposed KM++CSO method more segmentation efficiently. Furthermore, the interpretability is further enhanced using mathematical morphology and OTSU’s threshold. To this end, we tested the effectiveness of the KM++CSO methods on the mammogram image analysis society of the Mini–Mammographic Image Analysis Society (Mini–MIAS), the Digital Database for Screening Mammography (DDSM), and the Breast Cancer Digital Repository (BCDR) dataset through cross-validation. We maximize the accuracy and Jaccard index score, which is a measure that indicates the similarity between detected cancer and their corresponding reference cancer regions. The experimental results showed that the detection method obtained an accuracy of 96.42% (Mini–MIAS), 95.49% (DDSM), and 96.92% (BCDR). On overage, the KM++CSO method obtained 96.27% accuracy for three publicly available datasets. In addition, the detection results provided the 91.05% Jaccard index score.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Dataset and Image Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Wisaeng

2022

Diagnostics

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Anti-Cyberbullying System Using AI in Social Media

Hosanna,

Suguna

2024

Lecture Notes in Networks and Systems

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A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images

Sarvestani

Jamali

Taghizadeh

et al. 2023

J Cancer Res Clin Oncol

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Screening programs use mammography as a diagnostic tool for the early detection of breast cancer.Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalci cation on the mammographic image are two important indicators.The objective of this study was to evaluate the feasibility of automatic separation of images of breast tissue microcalci cations and also to evaluate its accuracy by using two techniques of image enhancement and highlighting of breast tissue microcalci cations for the desired areas by regional ROI based on fuzzy system and also Gabor ltering method. After determining the clusters of breast tissue microcalci cations, the clusters are classi ed using the decision tree classi cation algorithm. Then, for segmentation, samples suspected of microcalci cation are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of arti cial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous work. The results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%, which indicates that the proposed approach can be applied to ensure breast cancer diagnosis.

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Hybrid Segmentation and Feature Extraction Approach to Detect Tumour Based on Fuzzy Rough-in Mammogram Images

Cited by 7 publications

References 10 publications

Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization

Anti-Cyberbullying System Using AI in Social Media

A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images

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