Breast Cancer Lesion Detection and Classification in mammograms using Deep Neural

Breast cancer is the most common cancer among women in the world today. Mammography screening gives vital information about normal and abnormal regions. The task is to detect the lesion in mammograms using computer-aided diagnosis techniques. The automated detection of cancer decreases the mortality rate and manual error. In this work, the statistical (mean, variance, skewness, kurtosis, energy and entropy) and tamura features (coarseness, contrast and directionality) were extracted from the Cranial-Caudal (CC) view of mammogram images collected from the M.S. Ramaiah Memorial Hospital, Bangalore. The support vector machine was used for classification. Different support vector machine kernels were used and results were tabulated. The highest accuracy was obtained for linear and quadratic kernels with 95.7% with sensitivity of 100% and specificity of 91%.

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Feasibility of breast cancer lesion detection using a multi-frequency trans-admittance scanner (TAS) with 10 Hz to 500 kHz bandwidth

Physiological Measurement ◽

10.1088/0967-3334/28/7/s06 ◽

2007 ◽

Vol 28 (7) ◽

pp. S71-S84 ◽

Cited By ~ 17

Author(s):

Tong In Oh ◽

Jeehyun Lee ◽

Jin Keun Seo ◽

Sung Wan Kim ◽

Eung Je Woo

Keyword(s):

Breast Cancer ◽

Lesion Detection ◽

Cancer Lesion

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Leukocyte-mimicking nanovesicles for effective doxorubicin delivery to treat breast cancer and melanoma

Biomaterials Science ◽

10.1039/c9bm01766f ◽

2020 ◽

Vol 8 (1) ◽

pp. 333-341 ◽

Cited By ~ 7

Author(s):

Roberto Molinaro ◽

Jonathan O. Martinez ◽

Assaf Zinger ◽

Alessandro De Vita ◽

Gianluca Storci ◽

...

Keyword(s):

Breast Cancer ◽

Membrane Proteins ◽

Immune Cells ◽

Inflammatory Pathway ◽

Doxorubicin Delivery ◽

Cancer Lesion ◽

Treat Breast Cancer

Biomimetic nanovesicles deriving from leukocytes membrane proteins, called leukosomes, exhibit increased targeting of cancer vasculature and stroma by exploiting the inflammatory pathway responsible for recruiting immune cells to the cancer lesion.

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Evaluation of a Nonrigid Motion Compensation Technique Based on Spatiotemporal Features for Small Lesion Detection in Breast MRI

Advances in Artificial Neural Systems ◽

10.1155/2012/808602 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

F. Steinbruecker ◽

A. Meyer-Baese ◽

T. Schlossbauer ◽

D. Cremers

Keyword(s):

Breast Cancer ◽

Motion Compensation ◽

Rigid Motion ◽

Breast Mri ◽

Lesion Detection ◽

Nonrigid Motion ◽

Small Lesion ◽

Spatiotemporal Features ◽

Optical Flow Method ◽

Compensation Technique

Motion-induced artifacts represent a major problem in detection and diagnosis of breast cancer in dynamic contrast-enhanced magnetic resonance imaging. The goal of this paper is to evaluate the performance of a new nonrigid motion correction algorithm based on the optical flow method. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set under consideration of several 2D or 3D motion compensation parameters for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal motion compensation parameters. Our results have shown that motion compensation can improve the classification results. The results suggest that the computerized analysis system based on the non-rigid motion compensation technique and spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.

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Lesion Detection in Breast Tomosynthesis Using Efficient Deep Learning and Data Augmentation Techniques

10.3233/faia210150 ◽

2021 ◽

Author(s):

Loay Hassan ◽

Mohamed Abedl-Nasser ◽

Adel Saleh ◽

Domenec Puig

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Breast Lesion ◽

Data Augmentation ◽

Digital Breast Tomosynthesis ◽

Lesion Detection ◽

Detection Methods ◽

Breast Tomosynthesis ◽

Mammographic Images ◽

Augmentation Techniques

Digital breast tomosynthesis (DBT) is one of the powerful breast cancer screening technologies. DBT can improve the ability of radiologists to detect breast cancer, especially in the case of dense breasts, where it beats mammography. Although many automated methods were proposed to detect breast lesions in mammographic images, very few methods were proposed for DBT due to the unavailability of enough annotated DBT images for training object detectors. In this paper, we present fully automated deep-learning breast lesion detection methods. Specifically, we study the effectiveness of two data augmentation techniques (channel replication and channel-concatenation) with five state-of-the-art deep learning detection models. Our preliminary results on a challenging publically available DBT dataset showed that the channel-concatenation data augmentation technique can significantly improve the breast lesion detection results for deep learning-based breast lesion detectors.

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