Wazir Muhammad¹, Zuhaibuddin Bhutto^2,*, Salman Masroor^3,4, Murtaza Hussain Shaikh⁵, Jalal Shah², Ayaz Hussain¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1121-1142, 2023, DOI:10.32604/cmes.2023.021438

Abstract Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues. These challenges are increasing the interest in the quality of medical images. Recent research has proven that the rapid progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image super-resolution. However, the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory consumption. Furthermore, conventional deep CNN approaches used layers in series-wise connection to create the deeper mode, because this later end layer cannot… More >

Aghila Rajagopal¹, Sultan Ahmad^2,*, Sudan Jha³, Ramachandran Alagarsamy⁴, Abdullah Alharbi⁵, Bader Alouffi⁶

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3215-3229, 2023, DOI:10.32604/csse.2023.025705

Abstract Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada… More >

R. S. Sandhya Devi^1,*, V. R. Vijay Kumar², P. Sivakumar³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2249-2263, 2023, DOI:10.32604/csse.2023.032231

Abstract Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research. To perform the investigation on Plant and pest classification, Transfer Learning (TL) approach is used on EfficientNet-V2. TL requires limited labelled data and shorter training time. However, the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters. EfficientNet-V2 is a Convolutional Neural Network (CNN) model with significant high speed learning rates across variable sized datasets. The… More >

Anwer Mustafa Hilal^1,*, Eatedal Alabdulkreem², Jaber S. Alzahrani³, Majdy M. Eltahir⁴, Mohamed I. Eldesouki⁵, Ishfaq Yaseen¹, Abdelwahed Motwakel¹, Radwa Marzouk⁶

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1129-1143, 2023, DOI:10.32604/csse.2023.030080

Abstract Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image… More >

Yu Fan^1,6, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2, Chunyan Shao¹, Cheng Gong¹, Jieren Cheng^3,5, Yenwei Chen⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1279-1302, 2023, DOI:10.32604/cmc.2023.031445

Abstract Medical images are a critical component of the diagnostic process for clinicians. Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis, they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images. Traditional watermark embedding algorithm embeds the watermark information into the medical image, which reduces the quality of the medical image and affects the physicians’ judgment of patient diagnosis. In addition, watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed. This paper proposes… More >

He Gong^1,3,4, Mingwang Deng¹, Shijun Li^1,2,6,*, Tianli Hu^1,3,4, Yu Sun^1,3,4, Ye Mu^1,3,4, Zilian Wang¹, Chang Zhang¹, Thobela Louis Tyasi⁵

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4953-4969, 2022, DOI:10.32604/cmc.2022.027457

Abstract With the increasing intensive and large-scale development of the sika deer breeding industry, it is crucial to assess the health status of the sika deer by monitoring their behaviours. A machine vision–based method for the behaviour recognition of sika deer is proposed in this paper. Google Inception Net (GoogLeNet) is used to optimise the model in this paper. First, the number of layers and size of the model were reduced. Then, the 5 × 5 convolution was changed to two 3 × 3 convolutions, which reduced the parameters and increased the nonlinearity of the model. A 5 × 5 convolution… More >

Ali Alqahtani¹, Shumaila Akram², Muhammad Ramzan^2,3,*, Fouzia Nawaz², Hikmat Ullah Khan⁴, Essa Alhashlan⁵, Samar M. Alqhtani¹, Areeba Waris⁶, Zain Ali⁷

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1721-1736, 2023, DOI:10.32604/iasc.2023.025597

Abstract Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situation: one of the most common and effective methods is to examine the X-radiation (X-rays)… More >

K. Vijaya Kumar¹, E. Laxmi Lydia², Ashit Kumar Dutta³, Velmurugan Subbiah Parvathy⁴, Gobi Ramasamy⁵, Irina V. Pustokhina^6,*, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2541-2554, 2022, DOI:10.32604/cmc.2022.028570

Abstract Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,… More >

Zhilin Zhang¹, Ting Zhang¹, Zhaoying Liu^1,*, Peijie Zhang¹, Shanshan Tu¹, Yujian Li², Muhammad Waqas³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1527-1539, 2022, DOI:10.32604/cmc.2022.029297

Abstract The fine-grained ship image recognition task aims to identify various classes of ships. However, small inter-class, large intra-class differences between ships, and lacking of training samples are the reasons that make the task difficult. Therefore, to enhance the accuracy of the fine-grained ship image recognition, we design a fine-grained ship image recognition network based on bilinear convolutional neural network (BCNN) with Inception and additive margin Softmax (AM-Softmax). This network improves the BCNN in two aspects. Firstly, by introducing Inception branches to the BCNN network, it is helpful to enhance the ability of extracting comprehensive features from ships. Secondly, by adding… More >

Saleh Albahli^*

Computer Systems Science and Engineering, Vol.43, No.2, pp. 701-717, 2022, DOI:10.32604/csse.2022.022723

Abstract While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts… More >