Wan Azani Mustafa^1,2,*, Haniza Yazid³, Ahmed Alkhayyat⁴, Mohd Aminudin Jamlos³, Hasliza A. Rahim³, Midhat Nabil Salimi⁵

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5327-5342, 2022, DOI:10.32604/cmc.2022.023492

Abstract Luminosity and contrast variation problems are among the most challenging tasks in the image processing field, significantly improving image quality. Enhancement is implemented by adjusting the dark or bright intensity to improve the quality of the images and increase the segmentation performance. Recently, numerous methods had been proposed to normalise the luminosity and contrast variation. A new approach based on a direct technique using statistical data known as Hybrid Statistical Enhancement (HSE) is presented in this study. The HSE method uses the mean and standard deviation of a local and global neighbourhood and classified the pixel into three groups; the… More >

Abeer D. Algarni^1,*, Walid El-Shafai², Ghada M. El Banby³, Fathi E. Abd El-Samie^1,2, Naglaa F. Soliman^1,4

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4393-4410, 2022, DOI:10.32604/cmc.2022.020265

Abstract COVID-19 remains to proliferate precipitously in the world. It has significantly influenced public health, the world economy, and the persons’ lives. Hence, there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients. With this explosion of this pandemic, there is a need for automated diagnosis tools to help specialists based on medical images. This paper presents a hybrid Convolutional Neural Network (CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography (CT) images. The proposed approach is employed to classify and segment the COVID-19, pneumonia, and normal CT images. The classification stage is… More >

A. Sheryl Oliver¹, P. Suresh², A. Mohanarathinam³, Seifedine Kadry⁴, Orawit Thinnukool^5,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2031-2047, 2022, DOI:10.32604/cmc.2022.019876

Abstract Early diagnosis and detection are important tasks in controlling the spread of COVID-19. A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays. However, these methods suffer from biased results and inaccurate detection of the disease. So, the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network (OCOA-DDCNN) for COVID-19 prediction using CT images in IoT environment. The proposed methodology works on the basis of two stages such as pre-processing and prediction. Initially, CT scan images generated from prospective COVID-19 are collected… More >

P. Naga Srinivasu¹, Shakeel Ahmed^2,*, Abdulaziz Alhumam², Akash Bhoi Kumar³, Muhammad Fazal Ijaz⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3303-3319, 2021, DOI:10.32604/cmc.2021.018472

Abstract In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training, unlike in supervisory procedures requiring… More >

Walid El-Shafai¹, Noha A. El-Hag², Ghada M. El-Banby³, Ashraf A. M. Khalaf², Naglaa F. Soliman^4,*, Abeer D. Algarni⁴, Fathi E. Abd El-Samie^1,4

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1323-1341, 2021, DOI:10.32604/cmc.2021.017385

Abstract Corona Virus Disease-2019 (COVID-19) continues to spread rapidly in the world. It has dramatically affected daily lives, public health, and the world economy. This paper presents a segmentation and classification framework of COVID-19 images based on deep learning. Firstly, the classification process is employed to discriminate between COVID-19, non-COVID, and pneumonia by Convolutional Neural Network (CNN). Then, the segmentation process is applied for COVID-19 and pneumonia CT images. Finally, the resulting segmented images are used to identify the infected region, whether COVID-19 or pneumonia. The proposed CNN consists of four Convolutional (Conv) layers, four batch normalization layers, and four Rectified… More >

Abdul Majid¹, Muhammad Attique Khan¹, Yunyoung Nam^2,*, Usman Tariq³, Sudipta Roy⁴, Reham R. Mostafa⁵, Rasha H. Sakr⁶

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 319-337, 2021, DOI:10.32604/cmc.2021.016816

Abstract The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue in this area is the… More >

Mehedi Masud^*, Mohammad Dahman Alshehri, Roobaea Alroobaea, Mohammad Shorfuzzaman

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 1-13, 2021, DOI:10.32604/iasc.2021.016800

Abstract In 2020, the world faced an unprecedented pandemic outbreak of coronavirus disease (COVID-19), which causes severe threats to patients suffering from diabetes, kidney problems, and heart problems. A rapid testing mechanism is a primary obstacle to controlling the spread of COVID-19. Current tests focus on the reverse transcription-polymerase chain reaction (RT-PCR). The PCR test takes around 4–6 h to identify COVID-19 patients. Various research has recommended AI-based models leveraging machine learning, deep learning, and neural networks to classify COVID-19 and non-COVID patients from chest X-ray and computerized tomography (CT) scan images. However, no model can be claimed as a standard… More >

Yong Luo¹, Xiaojie Li², Chao Luo², Feng Wang¹, Xi Wu², Imran Mumtaz³, Cheng Yi^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1771-1780, 2020, DOI:10.32604/cmc.2020.010069

Abstract Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis. However, it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure. In this paper, we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net. In the proposed methodology, the network consists of two segmentation modules. The first module performs rough segmentation and the second module performs accurate segmentation. Considering the training time and the limitation of… More >

Xiaoya Guo¹, Dalin Tang^1,2,*, David Molony³, Chun Yang², Habib Samady³, Jie Zheng⁴, Gary S. Mintz⁵, Akiko Maehara⁵, Jian Zhu⁶, Genshan Ma⁶, Mitsuaki Matsumura⁵, Don P. Giddens^3,7

Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 75-76, 2019, DOI:10.32604/mcb.2019.05743

Abstract Atherosclerotic plaque progression is generally considered to be closely associated with morphological and mechanical factors. Plaque morphological information on intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images could complement each other and provide for more accurate plaque morphology. Fluid-structure interaction (FSI) models combining IVUS and OCT were constructed to obtain accurate plaque stress/strain and flow shear stress data for analysis. Accuracy and completeness of imaging and advanced modeling lead to accurate plaque progression predictions.
In vivo IVUS and OCT coronary plaque data at baseline and follow-up were acquired from left circumflex coronary and right coronary artery of one… More >

Zhe Liu¹, Charlie Maere^1,*, Yuqing Song¹

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 669-686, 2019, DOI:10.32604/cmc.2019.04590

Abstract Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing. We propose a novel approach of automatically identifying region of interest in Computed Tomography Image (CT) images based on temporal and spatial data . Our method is a 3 stages approach, 1) We extract organ features from the CT images by adopting the Hounsfield filter. 2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’ area and automatically detect a seed point. 3) We use a novel approach to track the growing region changes… More >