Automatic Detection of Masses From Mammographic Images via Artificial Intelligence Techniques

Azlan, Nur Aainaa Nadirah; Lu, Cheng-Kai; Elamvazuthi, Irraivan; Tang, Tong Boon

doi:10.1109/jsen.2020.3002559

Cited by 14 publications

(11 citation statements)

References 28 publications

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“…A comparison is also made with the algorithm of M. Hmida et al [28], where the algorithm was a hybrid between CVM and fuzzy cmeans, they dealt with 57 mammogram images of the class of masses only. N. A. N. Azlan et al [29], used normalization and filtration as a pre-processing before segmenting the image by the active contour method. The algorithms were implemented on all dataset images.…”

Section: Resultsmentioning

confidence: 99%

Breast Tumor Detection Via Active Contour Technique

Radhi¹,

Kamil²

2021

IJIES

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The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues. Segmentation algorithms often lose the path of tumor boundaries in an attempt to determine the position of them. Active contours are used widely for segmentation as a high-level technology for boundary recognition. The main aim to create a clear contrast between the tumour and the normal breast region. In this study, two approaches to active contour are applied: snakes and level sets. The proposed methods were applied to all abnormal mammogram images taken from mini-MIAS database. The first approach showed a weakness in the segmentation of this type of image, while the other approach was able to segment all the mammogram's tumours. The Chan-Vese method was the most superior of all the active contour segmentation methods. The proposed models were tested in two ways, the first is statistical the best result was for the Chan-Vese method and it came as follows (90%,95% 98%, 97%, 97%) for Jaccard, Dice, PF-Score, Precision, and Sensitivity respectively. And the other is based on the segmented region's characteristics, Chan-Vese was able to accurately determine the location and shape of the tumor. The proposed Chan-Vese approach is appropriate in adopting computer assisted detection systems to predict tumor boundaries and locations in mammography for its reliability and superior performance over other algorithms.

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Section: Resultsmentioning

confidence: 99%

Breast Tumor Detection Via Active Contour Technique

Radhi¹,

Kamil²

2021

IJIES

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“…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%

“…Vaka et al [ 11 ] presented end–to–end testing for breast cancer detection using Deep Neural Network with Support Value (DNNSV). Azlan et al [ 12 ] proposed Principal Component Analysis (PCA) and SVM for breast cancer detection. Next, George et al [ 13 ] developed hybrid methods by combining the CNN and SVM on the Breast Cancer Histopathological Database (BreakHis).…”

Section: Introductionmentioning

confidence: 99%

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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“…An accuracy of 92% achieved using this approach. In (Azlan et al, 2020), authors used wavelet filters for image denoising, which is followed by image enhancement using the top and bottom hat technique. Snake boundary detectors are used for segmentation, followed by augmentation to enhance the dataset.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network

Sharma

Purwar

2022

ADCAIJ

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This work proposes a technique of breast cancer detection from mammogram images. It is a multistage process which classifies the mammogram images into benign or malignant category. During preprocessing, images of Mammographic Image Analysis Society (MIAS) database are passed through a couple of filters for noise removal, thresholding and cropping techniques to extract the region of interest, followed by augmentation process on database to enhance its size. Features from Deep Convolution Neural Network (DCNN) are merged with texture features to form final feature vector. Using transfer learning, deep features are extracted from a modified DCNN, whose training is performed on 69% of randomly selected images of database from both categories. Features of Grey Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are merged to form texture features. Mean and variance of four parameters (contrast, correlation, homogeneity and entropy) of GLCM are computed in four angular directions, at ten distances. Ensemble Boosted Tree classifier using five-fold cross-validation mode, achieved an accuracy, sensitivity, specificity of 98.8%, 100% and 92.55% respectively on this feature vector.

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Automatic Detection of Masses From Mammographic Images via Artificial Intelligence Techniques

Cited by 14 publications

References 28 publications

Breast Tumor Detection Via Active Contour Technique

Breast Tumor Detection Via Active Contour Technique

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

Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network

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