Detection of breast cancer mass using MSER detector and features matching

Hassan, Shayma'a A.; Sayed, Mohammed S.; Abdalla, Mahmoud I.; Rashwan, Mohsen

doi:10.1007/s11042-019-7358-1

Cited by 20 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For breast mass classification, Hassan et al [119] employed two pre-trained CNN networks: AlexNet [117] and GoogleNet [120]. Mammogram images were pre-processed using the maximally stable extremal regions (MSER) [121] method. These CNN networks were trained and tested on mammogram images from CBIS-DDSM [122] and INbreast [113] databases, and they were also tested on the MIAS database [98] and real cases from the Egyptian National Cancer Institute.…”

Section: Cnn-based Detection/classification Methodsmentioning

confidence: 99%

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Saleh,

Batouty,

Gamal

et al. 2023

Cancers

View full text Add to dashboard Cite

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists’ proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists’ capabilities and ameliorating patient outcomes in the realm of breast cancer management.

show abstract

Section: Cnn-based Detection/classification Methodsmentioning

confidence: 99%

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Saleh,

Batouty,

Gamal

et al. 2023

Cancers

View full text Add to dashboard Cite

show abstract

“…This method improves the BC lesion classification for benign, malignant, and healthy patients with 89.47% of sensitivity and an accuracy of 90.5%. Hassan et al presented an automated algorithm for BC mass detection depending on the feature matching of different areas utilizing Maximally Stable Extremal Regions (MSER) 35 . The system was evaluated using 85 MIAS images, and it was 96.47% accurate in identifying the locations of masses.…”

Section: Related Workmentioning

confidence: 99%

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

Elkorany

Elsharkawy

2023

Sci Rep

View full text Add to dashboard Cite

Breast cancer (BC) is spreading more and more every day. Therefore, a patient's life can be saved by its early discovery. Mammography is frequently used to diagnose BC. The classification of mammography region of interest (ROI) patches (i.e., normal, malignant, or benign) is the most crucial phase in this process since it helps medical professionals to identify BC. In this paper, a hybrid technique that carries out a quick and precise classification that is appropriate for the BC diagnosis system is proposed and tested. Three different Deep Learning (DL) Convolution Neural Network (CNN) models—namely, Inception-V3, ResNet50, and AlexNet—are used in the current study as feature extractors. To extract useful features from each CNN model, our suggested method uses the Term Variance (TV) feature selection algorithm. The TV-selected features from each CNN model are combined and a further selection is performed to obtain the most useful features which are sent later to the multiclass support vector machine (MSVM) classifier. The Mammographic Image Analysis Society (MIAS) image database was used to test the effectiveness of the suggested method for classification. The mammogram's ROI is retrieved, and image patches are assigned to it. Based on the results of testing several TV feature subsets, the 600-feature subset with the highest classification performance was discovered. Higher classification accuracy (CA) is attained when compared to previously published work. The average CA for 70% of training is 97.81%, for 80% of training, it is 98%, and for 90% of training, it reaches its optimal value. Finally, the ablation analysis is performed to emphasize the role of the proposed network’s key parameters.

show abstract

“…Breast density was an important parameter considered by many tumor detection approaches. The seeded region growing algorithm used in [20] showed accurate results of mass detection under various breast densities. A feature-matching algorithm on various breast regions using the Maximally Stable Extremal Regions algorithm is shown in [21].…”

Section: B Mass Detection Algorithmsmentioning

confidence: 99%

WOMT: Wasserstein Distribution Based Minimization of False Positives in Breast Tumor Classification Using Deep Learning

Lakshmi,

Devi,

Gupta

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Women's life suffered and killed by invasive cancerous tumors is the most frequently highlighted header in many newsletters since 2010. There can be many. By their nature, invasive tumors spread from tissue to tissue and abduct themselves to cause new tumors. In many of the human biological parts, CT scan has given an objective approach to the early and successful detection of cancerous tumors. However, there were cases where diagnosis with CT scan images failed, resulting in many false positives. In economically backward countries, these false positives raised the notifying concern of women who cannot afford multiple diagnostic tests. Due to changes in biological metabolism, the growth of breast fat in women to considerably abnormal size is the main cause of false positives. In many of the images under study, this huge thick breast fat layer led to the rise of misclassification rate with false positives. Rendering societal help requires a precise mechanism that can reduce the false positives at an initial diagnosis. The proposed method introduces a novel constraint-based algorithm to classify a mammogram image as cancerous, aiming to reduce false positives. The proposed deep learning algorithm WOMT is trained with Wasserstein Distribution constraints that are derived from the mass transfer of cancerous patches to non-cancerous patches. The experimental simulations with a deep learning model trained with these constraints resulted in reduced false positives.

show abstract

Detection of breast cancer mass using MSER detector and features matching

Cited by 20 publications

References 20 publications

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

WOMT: Wasserstein Distribution Based Minimization of False Positives in Breast Tumor Classification Using Deep Learning

Contact Info

Product

Resources

About