Wei Song^1,2,3, Lihui Dong¹, Xiaobing Zhao^1,3, Jianxin Xia^4,*, Tongmu Liu⁵, Yuxi Shi⁶

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1649-1669, 2022, DOI:10.32604/cmc.2022.027214 - 18 May 2022

Abstract A large number of nodule minerals exist in the deep sea. Based on the factors of difficulty in shooting, high economic cost and high accuracy of resource assessment, large-scale planned commercial mining has not yet been conducted. Only experimental mining has been carried out in areas with high mineral density and obvious benefits after mineral resource assessment. As an efficient method for deep-sea mineral resource assessment, the deep towing system is equipped with a visual system for mineral resource analysis using collected images and videos, which has become a key component of resource assessment. Therefore,… More >

Wei Song^1,2,3, Haolin Wang¹, Xinping Zhang¹, Jianxin Xia^4,*, Tongmu Liu⁵, Yuxi Shi⁶

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1449-1462, 2022, DOI:10.32604/cmc.2022.027213 - 18 May 2022

Abstract Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and evaluation. The application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of mining. The existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly longer. In order to improve the segmentation performance of underwater mineral images, this paper uses the Pix2PixHD (Pixel to Pixel High Definition) algorithm based on Conditional Generative Adversarial Network (CGAN) to segment deep-sea… More >

Tran Manh Tuan^*, Tran Thi Ngan, Nguyen Tu Trung

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1241-1253, 2022, DOI:10.32604/csse.2022.024265 - 09 May 2022

Abstract In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order to perform next steps in image processing. Remote sensing images usually have large size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detect objects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is… More >

P. Indra, G. Kavithaa^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 877-893, 2022, DOI:10.32604/iasc.2022.025982 - 03 May 2022

Abstract Image processing innovations assume a significant part in diagnosing and distinguishing diseases and monitoring these diseases’ quality. In Medical Images, detection of breast cancer in its earlier stage is most important in this field. Because of the low contrast and uncertain design of the tumor cells in breast images, it is still challenging to classify breast tumors only by visual testing by the radiologists. Hence, improvement of computer-supported strategies has been introduced for breast cancer identification. This work presents an efficient computer-assisted method for breast cancer classification of digital mammograms using Adaptive Resource Allocation Network… More >

Weiwei Cai^1,2, Yaping Song¹, Huan Duan¹, Zhenwei Xia¹, Zhanguo Wei^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1539-1555, 2022, DOI:10.32604/cmes.2022.019785 - 19 April 2022

Abstract In the smart logistics industry, unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans. Therefore, they play a critical role in smart warehousing, and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets. However, most current recognition algorithms are ineffective due to the diverse types of pallets, their complex shapes, frequent blockades in production environments, and changing lighting conditions. This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention (MFMBA) neural network for logistics… More >

Linguo Li^1,2, Xuwen Huang², Shunqiang Qian², Zhangfei Li², Shujing Li^2,*, Romany F. Mansour³

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3073-3090, 2022, DOI:10.32604/cmc.2022.026625 - 29 March 2022

Abstract In order to address the problems of Coyote Optimization Algorithm in image thresholding, such as easily falling into local optimum, and slow convergence speed, a Fuzzy Hybrid Coyote Optimization Algorithm (hereinafter referred to as FHCOA) based on chaotic initialization and reverse learning strategy is proposed, and its effect on image thresholding is verified. Through chaotic initialization, the random number initialization mode in the standard coyote optimization algorithm (COA) is replaced by chaotic sequence. Such sequence is nonlinear and long-term unpredictable, these characteristics can effectively improve the diversity of the population in the optimization algorithm. Therefore,… More >

Fikret Yalcinkaya^*, Ali Erbas

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1961-1977, 2022, DOI:10.32604/iasc.2022.024961 - 24 March 2022

Abstract Early stage melanoma detection (ESMD) is crucial as late detection kills. Computer aided diagnosis systems (CADS) integrated with high level algorithms are major tools capable of ESMD with high degree of accuracy, specificity, and sensitivity. CADS use the image and the information within the pixels of the image. Pixels’ characteristics and orientations determine the colour and shapes of the images as the pixels and associated environment are closely interrelated with the lesion. CADS integrated with Convolutional Neural Networks (CNN) specifically play a major role for ESMD with high degree of accuracy. The proposed system has… More >

J. Anitha^*, M. Kalaiarasu

Computer Systems Science and Engineering, Vol.43, No.1, pp. 363-379, 2022, DOI:10.32604/csse.2022.022402 - 23 March 2022

Abstract Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and… More >

Jia Chen¹, Zhiqiang He¹, Dayong Zhu¹, Bei Hui^1,*, Rita Yi Man Li², Xiao-Guang Yue^3,4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 73-95, 2022, DOI:10.32604/cmes.2022.018565 - 24 January 2022

Abstract Medical image segmentation plays an important role in clinical diagnosis, quantitative analysis, and treatment process. Since 2015, U-Net-based approaches have been widely used for medical image segmentation. The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps. However, the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information. More high-level information can make the segmentation more accurate. In this paper, we propose MU-Net, a novel, multi-path upsampling convolution network to retain more high-level information. The MU-Net mainly More >

Maham Khan¹, Syed Adnan Shah¹, Tenvir Ali², Quratulain², Aymen Khan², Gyu Sang Choi^3,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5005-5020, 2022, DOI:10.32604/cmc.2022.023007 - 14 January 2022

Abstract Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification… More >