Mammographic Image Based Breast Tissue Classification with Kernel Self-optimized Fisher Discriminant for Breast Cancer Diagnosis

Li, Junbao

doi:10.1007/s10916-011-9691-4

“…Focusing on obtaining good mammogram images as an objective, the authors of [17] present the connected component labelling analysis (CCL), which is a multi-level segmentation approach based on artefacts and pectoral muscle removal to ensure good clarity of mammogram images. For the feature extraction and classification stages, they use statistical and Kernel self-optimized fisher discriminant (KSFD) methods, respectively.…”

Section: B Taxonomy Of Ai-based Approaches For Breast Cancer Diagnosismentioning

confidence: 99%

“…From a medical point of view, the early detection of breast cancer contributes to saving the lives of patients as well as decreasing the cost of treatment at both the private and governmental medical institution levels. Computer science researchers have employed AI for this purpose, and many approaches have been proposed, such as [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. However, the quality of any proposed approach for breast cancer detection is evaluated based on its accuracy.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Alfifi¹,

Alrahhal²,

Bataineh³

et al. 2020

IJACSA

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Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics.

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“…From a medical point of view, the early detection of breast cancer contributes to saving the lives of patients as well as decreasing the cost of treatment at both the private and www.thesai.org governmental medical institution levels. Computer science researchers have employed AI for this purpose, and many approaches have been proposed, such as [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. However, the quality of any proposed approach for breast cancer detection is evaluated based on its accuracy.…”

Section: Statement Of Problemmentioning

confidence: 99%

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer Using Deep Learning

AISDL¹

2020

Preprint

0

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Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics.

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“…In another study, Moayedi et al used the logarithm of the pixel energies for pectoral muscle removal [28]. Mean shift segmentation [29], connected component labeling [30], RGM [31][32][33][34], RGM combined with geometric rules [35], and fuzzy c-means clustering [36] are some examples of other methods used for pectoral muscle removal in the literature.…”

Section: Related Workmentioning

confidence: 99%

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Esener¹,

Ergin²,

Yüksel³

2018

Turk J Elec Eng & Comp Sci

12

0

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Abstract:In this paper, a novel multistage scheme for pectoral muscle removal from mammography images is proposed, and the performance of this system is verified using the publicly available Mammographic Image Analysis Society digital mammogram database. This database is composed of mediolateral oblique mammography images including three different tissue types (fatty, fatty-glandular, and dense-glandular) with three health status types (normal, benign cancer, and malignant cancer). In the implementation of the proposed system, a mammography image is first preprocessed by performing noise reduction background removal followed by artifact suppression processes. Then a presegmentation procedure is applied using region growing and line fitting is executed. Finally, pixels including pectoral muscle regions are removed from mammography images with an accuracy of 94.40%, sensitivity of 89.62%, and specificity of 99.99% after some postprocessing operations. Although the mean false positive rate obtained by the proposed approach is higher than that of other studies in the literature, not only the lower mean false negative rate but also the enhancement in the quality of pectoral muscle segmentation for all 322 images in the database evidently show the success of the proposed approach.

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Mammographic Image Based Breast Tissue Classification with Kernel Self-optimized Fisher Discriminant for Breast Cancer Diagnosis

Cited by 21 publications

References 16 publications

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer Using Deep Learning

A novel multistage system for the detection and removal of pectoral muscles in mammograms

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