Ishu Anand¹, Himani Negi¹, Deepika Kumar¹, Mamta Mittal², Tai-hoon Kim^3,*, Sudipta Roy⁴

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3107-3127, 2021, DOI:10.32604/cmc.2021.014229

Abstract Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images.… More >

Runzhe Tao^{1, *}, Yonghong Zhang¹, Lihua Wang¹, Pengyan Cai¹, Haowen Tan²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2455-2474, 2020, DOI:10.32604/cmc.2020.011526

Abstract Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring. UNet, an advanced machine learning (ML) method, is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A (FY-4A). First, in this algorithm, the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model (DEM) has been used as predictor variables for our model. Second, the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of… More >

Hui Li¹, Chengwei Pan^{2, 3}, Ziyi Chen¹, Aziguli Wulamu^{2, 3, *}, Alan Yang⁴

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 563-578, 2020, DOI:10.32604/cmc.2020.09806

Abstract Ore image segmentation is a key step in an ore grain size analysis based on image processing. The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation. In this article, in order to solve the problem, an ore image segmentation method based on U-Net is proposed. We adjust the structure of U-Net to speed up the processing, and we modify the loss function to enhance the generalization of the model. After the collection of the ore image, we design the annotation standard and train the network… More >

Yi Shen¹, Victor S. Sheng^{1, 2, *}, Lei Wang¹, Jie Duan¹, Xuefeng Xi¹, Dengyong Zhang³, Ziming Cui¹

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1233-1247, 2020, DOI:10.32604/cmc.2020.07450

Abstract Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases. In recent years, due to the great improvement of hard device, many deep learning based methods have been proposed for automatic liver segmentation. Among them, there are the plain neural network headed by FCN and the residual neural network headed by Resnet, both of which have many variations. They have achieved certain achievements in medical image segmentation. In this paper, we firstly select five representative structures, i.e., FCN, U-Net, Segnet, Resnet and Densenet, to investigate their performance on liver… More >

Caining Zhang¹, Huaguang Li², Xiaoya Guo³, David Molony⁴, Xiaopeng Guo², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Rencan Nie^2,*, Jinde Cao^3,*, Dalin Tang^1,*,7

Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 31-31, 2019, DOI:10.32604/mcb.2019.06983

Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5… More >

Caining Zhang¹, Huaguang Li², Xiaoya Guo³, David Molony⁴, Xiaopeng Guo², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Rencan Nie^2,*, Jinde Cao^3,*, Dalin Tang^1,*,7

Molecular & Cellular Biomechanics, Vol.16, No.2, pp. 153-161, 2019, DOI:10.32604/mcb.2019.06873

Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5… More >