Sixian Chan^1,2, Binghui Wu¹, Guodao Zhang³, Yuan Yao⁴, Hongqiang Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 411-428, 2024, DOI:10.32604/cmes.2023.029485 - 22 September 2023

Abstract In clinical practice, the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications. Measuring the amount of each type of urine sediment allows for screening, diagnosis and evaluation of kidney and urinary tract disease, providing insight into the specific type and severity. However, manual urine sediment examination is labor-intensive, time-consuming, and subjective. Traditional machine learning based object detection methods require hand-crafted features for localization and classification, which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments. Deep learning based object detection… More > Graphic Abstract

Yuling Dou¹, Fengqin Yao¹, Xiandong Wang¹, Liang Qu², Long Chen³, Zhiwei Xu⁴, Laihui Ding⁴, Leon Bevan Bullock¹, Guoqiang Zhong¹, Shengke Wang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1001-1014, 2024, DOI:10.32604/cmes.2023.027764 - 22 September 2023

Abstract UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience, low cost and convenient maintenance. In marine environmental monitoring, the similarity between objects such as oil spill and sea surface, Spartina alterniflora and algae is high, and the effect of the general segmentation algorithm is poor, which brings new challenges to the segmentation of UAV marine images. Panoramic segmentation can do object detection and semantic segmentation at the same time, which can well solve the polymorphism problem of objects in UAV ocean images. Currently, there are few studies on… More >

Ning Li^*, Shucai Huang, Daozhi Wei

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3511-3531, 2023, DOI:10.32604/cmc.2023.045987 - 26 December 2023

Abstract This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet (ISTD-CenterNet) network for detecting small infrared targets in complex environments. The method eliminates the need for an anchor frame, addressing the issues of low accuracy and slow speed. HRNet is used as the framework for feature extraction, and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects. A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire… More >

Muhammad Mumtaz Ali, Faiqa Maqsood, Shiqi Liu, Weiyan Hou, Liying Zhang, Zhenfei Wang^*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2699-2714, 2023, DOI:10.32604/cmc.2023.045310 - 26 December 2023

Abstract Breast cancer, particularly Invasive Ductal Carcinoma (IDC), is a primary global health concern predominantly affecting women. Early and precise diagnosis is crucial for effective treatment planning. Several AI-based techniques for IDC-level classification have been proposed in recent years. Processing speed, memory size, and accuracy can still be improved for better performance. Our study presents ECAM, an Enhanced Channel-Wise Attention Mechanism, using deep learning to analyze histopathological images of Breast Invasive Ductal Carcinoma (BIDC). The main objectives of our study are to enhance computational efficiency using a Separable CNN architecture, improve data representation through hierarchical feature… More >

Bauyrzhan Omarov^1,*, Zhuldyz Sailaukyzy², Alfiya Bigaliyeva², Adilzhan Kereyev³, Lyazat Naizabayeva⁴, Aigul Dautbayeva⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3765-3781, 2023, DOI:10.32604/cmc.2023.042469 - 26 December 2023

Abstract In the face of escalating intricacy and heterogeneity within Internet of Things (IoT) network landscapes, the imperative for adept intrusion detection techniques has never been more pressing. This paper delineates a pioneering deep learning-based intrusion detection model: the One Dimensional Convolutional Neural Networks (1D-CNN) and Bidirectional Long Short-Term Memory (BiLSTM) Network (Conv-BiLSTM) augmented with an Attention Mechanism. The primary objective of this research is to engineer a sophisticated model proficient in discerning the nuanced patterns and temporal dependencies quintessential to IoT network traffic data, thereby facilitating the precise categorization of a myriad of intrusion types. Methodology:… More >

Jin Su Kim, Sungbum Pan^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1663-1678, 2023, DOI:10.32604/cmc.2023.043998 - 29 November 2023

Abstract As Internet of Things (IoT) technology develops, integrating network functions into diverse equipment introduces new challenges, particularly in dealing with counterfeit issues. Over the past few decades, research efforts have focused on leveraging electromyogram (EMG) for personal recognition, aiming to address security concerns. However, obtaining consistent EMG signals from the same individual is inherently challenging, resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition. Notably, conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities. This paper proposes an innovative approach to personal recognition that combines a siamese… More >

Xiaoqi Xin^*, Dezhi Han, Mingming Cui

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2407-2425, 2023, DOI:10.32604/cmc.2023.042308 - 29 November 2023

Abstract Person Search is a task involving pedestrian detection and person re-identification, aiming to retrieve person images matching a given objective attribute from a large-scale image library. The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively. The current popular Person Search models, whether end-to-end or two-step, are based on anchor boxes. However, due to the limitations of the anchor itself, the model inevitably has some disadvantages, such as unbalance of positive and negative samples and redundant calculation, which will affect… More >

Fufang Li, Manlin Luo^*, Ming Hu, Guobin Wang, Yan Chen

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2835-2850, 2023, DOI:10.32604/csse.2023.039765 - 09 November 2023

Abstract Liver cancer has the second highest incidence rate among all types of malignant tumors, and currently, its diagnosis heavily depends on doctors’ manual labeling of CT scan images, a process that is time-consuming and susceptible to subjective errors. To address the aforementioned issues, we propose an automatic segmentation model for liver and tumors called Res2Swin Unet, which is based on the Unet architecture. The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation, respectively. Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections, while Swin Transformer captures long-range More >

Yixin Tang^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 867-891, 2023, DOI:10.32604/cmc.2023.041550 - 31 October 2023

Abstract Cartoons serve as significant sources of entertainment for children and adolescents. However, numerous animated videos contain unsuitable content, such as violence, eroticism, abuse, and vehicular accidents. Current content detection methods rely on manual inspection, which is resource-intensive, time-consuming, and not always reliable. Therefore, more efficient detection methods are necessary to safeguard young viewers. This paper addresses this significant problem by proposing a novel deep learning-based system, AnimeNet, designed to detect varying degrees of violent and erotic content in videos. AnimeNet utilizes a novel Convolutional Neural Network (CNN) model to extract image features effectively, classifying violent… More >

P. Kalaiselvi^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1209-1226, 2023, DOI:10.32604/cmc.2023.040264 - 31 October 2023

Abstract In recent days, Deep Learning (DL) techniques have become an emerging transformation in the field of machine learning, artificial intelligence, computer vision, and so on. Subsequently, researchers and industries have been highly endorsed in the medical field, predicting and controlling diverse diseases at specific intervals. Liver tumor prediction is a vital chore in analyzing and treating liver diseases. This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed work aims to improve accuracy and robustness in diagnosing… More >