Mahmoud Ragab^1,2,3,*, Jaber Alyami^4,5

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2309-2322, 2023, DOI:10.32604/csse.2023.026877

Abstract Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent… More >

M. Vinoth Kumar^1,*, M. Prakash², M. Naresh Kumar³, H. Abdul Shabeer⁴

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1277-1293, 2023, DOI:10.32604/iasc.2023.025591

Abstract The most noteworthy neurodegenerative disorder nationwide is apparently the Alzheimer's disease (AD) which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy, a sensitive method for evaluating the AD has to be developed yet. Due to the correlations between ocular and brain tissue, the eye (retinal blood vessels) has been investigated for predicting the AD. Hence, en enhanced method named Enhanced Long Short Term Memory (E-LSTM) has been proposed in this work which aims at finding the severity of AD from ocular biomarkers. To find the level of disease severity, the… More >

Walid El-Shafai^1,2, Amira A. Mahmoud¹, El-Sayed M. El-Rabaie¹, Taha E. Taha¹, Osama F. Zahran¹, Adel S. El-Fishawy¹, Naglaa F. Soliman³, Amel A. Alhussan^4,*, Fathi E. Abd El-Samie¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3455-3472, 2022, DOI:10.32604/cmc.2022.028722

Abstract The segmentation process requires separating the image region into sub-regions of similar properties. Each sub-region has a group of pixels having the same characteristics, such as texture or intensity. This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization (PSO) and improved fast fuzzy C-means clustering (IFFCM) algorithms. An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques. The existing medical image segmentation techniques incorporate clustering, thresholding, graph-based, edge-based, active contour, region-based, and watershed algorithms. This paper extensively analyzes and summarizes the… More >

Shouming Hou¹, Chaolan Jia¹, Kai Li¹, Liya Fan², Jincheng Guo^3,*, Mackenzie Brown^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 81-94, 2022, DOI:10.32604/cmes.2022.019006

Abstract Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID-19) CT images is extremely important. Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB). As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The edge of CT images is… More >

Mohammad Alamgeer¹, Hanan Abdullah Mengash², Radwa Marzouk², Mohamed K Nour³, Anwer Mustafa Hilal^4,*, Abdelwahed Motwakel⁴, Abu Sarwar Zamani⁴, Mohammed Rizwanullah⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1437-1448, 2022, DOI:10.32604/cmc.2022.027896

Abstract Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images. The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix (GLCM) model for feature… More >

Afef Houimli^1,*, IssamBen Mhamed², Bechir Letaief^1,3,4, Dorra Ben-Sellem^1,3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 881-904, 2022, DOI:10.32604/cmes.2022.016705

Abstract In Single-Photon Emission Computed Tomography (SPECT), the reconstructed image has insufficient contrast, poor resolution and inaccurate volume of the tumor size due to physical degradation factors. Generally, nonstationary filtering of the projection or the slice is one of the strategies for correcting the resolution and therefore improving the quality of the reconstructed SPECT images. This paper presents a new 3D algorithm that enhances the quality of reconstructed thoracic SPECT images and reduces the noise level with the best degree of accuracy. The suggested algorithm is composed of three steps. The first one consists of denoising the acquired projections using the… More >

R. Kanthavel^1,*, R. Dhaya², Kanagaraj Venusamy³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5783-5798, 2022, DOI:10.32604/cmc.2022.023745

Abstract Knee Osteoarthritis (OA) is a joint disease that is commonly observed in people around the world. Osteoarthritis commonly affects patients who are obese and those above the age of 60. A valid knee image was generated by Computed Tomography (CT). In this work, efficient segmentation of CT images using Elephant Herding Optimization (EHO) optimization is implemented. The initial stage employs, the CT image normalization and the normalized image is incited to image enhancement through histogram correlation. Consequently, the enhanced image is segmented by utilizing Niblack and Bernsen algorithm. The (EHO) optimized outcome is evaluated in two steps. The initial step… More >

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 >