D. Banumathy^1,*, Osamah Ibrahim Khalaf², Carlos Andrés Tavera Romero³, P. Vishnu Raja⁴, Dilip Kumar Sharma⁵

Computer Systems Science and Engineering, Vol.48, No.3, pp. 863-866, 2024, DOI:10.32604/csse.2024.053657 - 20 May 2024

Abstract This article has no abstract. More >

Afnan M. Alhassan^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2207-2223, 2024, DOI:10.32604/cmc.2024.046427 - 27 February 2024

Abstract Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BAC detection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has… More >

P. Gomathi^1,*, C. Muniraj², P. S. Periasamy³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1887-1899, 2023, DOI:10.32604/csse.2023.028808 - 03 November 2022

Abstract The mortality rate decreases as the early detection of Breast Cancer (BC) methods are emerging very fast, and when the starting stage of BC is detected, it is curable. The early detection of the disease depends on the image processing techniques, and it is used to identify the disease easily and accurately, especially the micro calcifications are visible on mammography when they are 0.1 mm or bigger, and cancer cells are about 0.03 mm, which is crucial for identifying in the BC area. To achieve this micro calcification in the BC images, it is necessary… More >

D. Banumathy^1,*, Osamah Ibrahim Khalaf², Carlos Andrés Tavera Romero³, P. Vishnu Raja⁴, Dilip Kumar Sharma⁵

Computer Systems Science and Engineering, Vol.44, No.1, pp. 595-612, 2023, DOI:10.32604/csse.2023.025611 - 01 June 2022

Abstract The most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives. The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time. This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient… More >

Ram P. Ghosh¹, Matteo Bianchi¹, Gil Marom², Oren M. Rotman¹, Brandon Kovarovic¹, Danny Bluestein^1,*

Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 12-14, 2019, DOI:10.32604/mcb.2019.07379

Abstract This article has no abstract. More >

Shu Q. Liu^1,*

Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 5-7, 2019, DOI:10.32604/mcb.2019.06960

Abstract This article has no abstract. More >

Yuliya Vengrenyuk^*, Luis Cardoso^*, Sheldon Weinbaum^∗,†

Molecular & Cellular Biomechanics, Vol.5, No.1, pp. 37-48, 2008, DOI:10.3970/mcb.2008.005.037

Abstract In this paper, we further investigate the new paradigm for the rupture of thin cap fibroatheroma (TCFA) proposed in Vengrenyuk et al. (2006 PNAS 103:14678) using a multilevel micro-CT based 3D numerical modeling. The new paradigm proposes that the rupture of TCFA is due to stress-induced interfacial debonding of cellular - level, 10 -- 20 μm microcalcifications in the fibrous cap proper. Such microcalcifications, which lie below the visibility of current in vivo imaging techniques, were detected for the first time using confocal microscopy and high resolution microcomputed tomography (micro-CT) imaging in Vengrenyuk et al. (2006) In the present… More >