Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector

Divyashree, B. V.; Kumar, G. Hemantha

doi:10.1007/s42979-021-00452-8

Cited by 14 publications

(5 citation statements)

References 31 publications

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“…The existing model such as the computer-aided diagnosis (CAD) [16] method has some limitations which consume more time while training multiple classifiers. Gray difference weight and MSER detector [17] has few risks in some images which has no significant contrast pectoral muscle. The proposed weight-based AdaBoost algorithm achieved better performance by combining the AlexNet and ResNet50 architectures in the feature extraction and increasing the weights in the AdaBoost algorithm.…”

Section: Discussionmentioning

confidence: 99%

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An Early Detection and Classification of Breast Cancer Using Weight based AdaBoost Algorithm

2023

IJIES

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Breast cancer is a deadly disease; an accurate and early diagnosis of breast cancer is the most efficient method to decrease the death rate. But, in the early detection and diagnosis of breast cancer, differentiating abnormal tissues is a challenging task. In this paper, a weight-based AdaBoost algorithm is proposed for an effective detection and classification of the breast cancer. An AdaBoost algorithm effectively classifies the breast cancer classes by adding the weights to the samples in the week classifier during the training phase. A weighted vote is performed on the results of each week classifier, and the strong classifier is integrated according to the weight of the week classifier. The breast cancer image datasets named CBIS-DDSM and MIAS are utilized for effective classification. Tumor-like regions (TLRs) are diagnosed by utilizing the optimum method of Otsu thresholding to enhance training abilities. The convolutional neural network (CNN) architectures of the AlexNet and ResNet50 are utilized for the feature extraction. A weight-based AdaBoost algorithm is proposed for the classification of breast cancer mammogram images into four classes benign calcification (BC), malignant calcification (MC), benign mass (BM) as well and malignant mass (MM).The results shows that the proposed weight based AdaBoost algorithm delivers the performance metrics such as accuracy, specificity, sensitivity, precision and F1-score values about 99.56%, 99.38%, 99.40%, 98.89% and 99.18% respectively, which ensures the accurate classification results compared with the existing methods such as IMPA-ResNet50, Gray difference weight and MSER detector, MLO and CC methods.

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Section: Discussionmentioning

confidence: 99%

“…Divyashree and Kumar [17] performed background supervision and pectoral muscle removal utilizing a gray difference weight and MSER detector. A contrast-limited adaptive histogram equalization (CLAHE) and de-correlation stretch were performed to improve the detection of breast region.…”

Section: Literature Surveymentioning

confidence: 99%

An Early Detection and Classification of Breast Cancer Using Weight based AdaBoost Algorithm

2023

IJIES

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“…Also, the images are padded so that the image size is 1024 x 1024 pixels. The format of the images is Portable Gray Map (PGM), a special format with image data in the form of grayscale maps (22) . Additionally, the dataset contains information about the character of the background tissue, classifying them as fatty, fatty-glandular and dense-glandular, as well as information regarding the type of abnormality such as calcification, well-defined or circumscribed masses, spiculated, ill-defined, architectural distortion, asymmetry or normality.…”

Section: Medical Images Selection and Preprocessingmentioning

confidence: 99%

approach to edge detection in medical imaging through histogram analysis and morphological gradient

Rondón¹,

Carvajal

Casadiego³

et al. 2022

inycomp

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Edge detection takes importance in image processing systems for computer-aided diagnosis, wheresharp changes in pixel intensity are analyzed to obtain fast and accurate information about regions ofinterest to the specialist. A method for feature enhancement and edge detection in medical imageswas developed using image processing by analyzing the pixel distribution histogram andmorphological gradient operation. Images from the MINI MIAS dataset and the COVID-CT datasetwere used. The method is based on image processing and is applied to mammography and chest CTimages, where blur filtering steps are accompanied by morphological gradient filtering, in addition toobtaining the threshold for edge detection by analyzing the point of maximum pixel concentrationaccording to the distribution histogram. The processing is presented in a graphical user interfacedeveloped in Python language. The method is validated by comparison with other edge detectiontechniques such as the Canny Algorithm, and with deep learning methods such as Holistically-NestedEdge Detection. The proposed method improves image quality in both mammograms and CT scanscompared to other techniques. It also presents the best performance considering internal and externaledge detection, as well as an average response time of 1.054 seconds and 2.63 % of Central Processing Unit requirement. The developed system is presented as a support tool for use in computer-aideddiagnosis processes due to its high efficiency in edge detection.

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“…Additionally, the MRI images are a highly recommended test to monitor and detect the breast cancer lesion and to interpret the lesioned region, because it has better soft tissue imaging [9]. Additionally, an experienced physician is needed to process the MRI images, which is a time-consuming mechanism [10], [11]. For overcoming the above-stated issue, several automated models are Int J Elec & Comp Eng ISSN: 2088-8708  implemented by the researchers [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Kumar Mamdy,

Petli

2024

IJECE

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Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.

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Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector

Cited by 14 publications

References 31 publications

An Early Detection and Classification of Breast Cancer Using Weight based AdaBoost Algorithm

An Early Detection and Classification of Breast Cancer Using Weight based AdaBoost Algorithm

approach to edge detection in medical imaging through histogram analysis and morphological gradient

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

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