Sitara Afzal, Imran Ullah Khan, Jong Weon Lee^*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1903-1923, 2022, DOI:10.32604/cmc.2022.021930 - 03 November 2021

Abstract Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum vessels. Individuals and elderly people with brain injury and dementia can have small microbleeds in their brains. A recent study has shown that cerebral microbleeds could be remarkably risky in terms of life and can be riskier for patients with dementia. In this study, we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging (SWI) data samples. The proposed structure comprises two different pre-trained convolutional models with four stages. These stages… More >

Nadia H. Alsulami^1,*, Amani T. Jamal¹, Lamiaa A. Elrefaei²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 85-108, 2022, DOI:10.32604/cmc.2022.019450 - 03 November 2021

Abstract Lip-reading technologies are rapidly progressing following the breakthrough of deep learning. It plays a vital role in its many applications, such as: human-machine communication practices or security applications. In this paper, we propose to develop an effective lip-reading recognition model for Arabic visual speech recognition by implementing deep learning algorithms. The Arabic visual datasets that have been collected contains 2400 records of Arabic digits and 960 records of Arabic phrases from 24 native speakers. The primary purpose is to provide a high-performance model in terms of enhancing the preprocessing phase. Firstly, we extract keyframes from… More >

Haleem Farman¹, Jamil Ahmad^1,*, Bilal Jan², Yasir Shahzad³, Muhammad Abdullah¹, Atta Ullah⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 2073-2089, 2022, DOI:10.32604/cmc.2022.018961 - 03 November 2021

Abstract Plant diseases are a major cause of degraded fruit quality and yield losses. These losses can be significantly reduced with early detection of diseases to ensure their timely treatment, particularly in developing countries. In this regard, an expert system based on deep learning model where the expert knowledge, particularly the one acquired by plant pathologist, is recursively learned by the system and is applied using a smart phone application for use in the target field environment, is being proposed. In this paper, a robust disease detection method is developed based on convolutional neural network (CNN),… More >

Kuntha Pin¹, Jee Ho Chang², Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5821-5834, 2022, DOI:10.32604/cmc.2022.021943 - 11 October 2021

Abstract While the usage of digital ocular fundus image has been widespread in ophthalmology practice, the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly. We explored a robust deep learning system that detects three major ocular diseases: diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD). The proposed method is composed of two steps. First, an initial quality evaluation in the classification system is proposed to filter out poor-quality images to enhance its performance, a technique that has not been explored previously. Second, the transfer learning… More >

Taher M. Ghazal^1,2, Sagheer Abbas³, Sundus Munir^3,4, M. A. Khan⁵, Munir Ahmad³, Ghassan F. Issa², Syeda Binish Zahra⁴, Muhammad Adnan Khan^6,*, Mohammad Kamrul Hasan¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5005-5019, 2022, DOI:10.32604/cmc.2022.020866 - 11 October 2021

Abstract Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems More >

Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi^*

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1393-1403, 2022, DOI:10.32604/iasc.2022.020629 - 09 October 2021

Abstract The detection of fires in surveillance videos are usually done by utilizing deep learning. In Spite of the advances in processing power, deep learning methods usually need extensive computations and require high memory resources. This leads to restriction in real time fire detection. In this research, we present a time-efficient fire detection convolutional neural network coupled with transfer learning for surveillance systems. The model utilizes CNN architecture with reasonable computational time that is deemed possible for real time applications. At the same time, the model will not compromise accuracy for time efficiency by tuning the More >

Hanan A. Hosni Mahmoud, Norah S. Alghamdi, Amal H. Alharbi^*

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1423-1434, 2022, DOI:10.32604/iasc.2022.020586 - 09 October 2021

Abstract COVID-19 pandemic outbreak became one of the serious threats to humans. As there is no cure yet for this virus, we have to control the spread of Coronavirus through precautions. One of the effective precautions as announced by the World Health Organization is mask wearing. Surveillance systems in crowded places can lead to detection of people wearing masks. Therefore, it is highly urgent for computerized mask detection methods that can operate in real-time. As for now, most countries demand mask-wearing in public places to avoid the spreading of this virus. In this paper, we are… More >

Mehdi Hassan^1,*, Safdar Ali², Muhammad Sanaullah³, Khuram Shahzad⁴, Sadaf Mushtaq^5,6, Rashda Abbasi⁶, Zulqurnain Ali⁴, Hani Alquhayz⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2743-2760, 2022, DOI:10.32604/cmc.2022.020055 - 27 September 2021

Abstract Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are… More >

Juan Pablo Villa-Pulgarin¹, Anderson Alberto Ruales-Torres^1,2, Daniel Arias-Garzón¹, Mario Alejandro Bravo-Ortiz¹, Harold Brayan Arteaga-Arteaga¹, Alejandro Mora-Rubio¹, Jesus Alejandro Alzate-Grisales¹, Esteban Mercado-Ruiz¹, M. Hassaballah³, Simon Orozco-Arias^4,5, Oscar Cardona-Morales¹, Reinel Tabares-Soto^1,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2131-2148, 2022, DOI:10.32604/cmc.2022.019529 - 27 September 2021

Abstract Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models More >

Shoroog Khenkar^1,*, Salma Kammoun Jarraya^1,2

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2655-2677, 2022, DOI:10.32604/cmc.2022.019152 - 27 September 2021

Abstract This paper proposes a novel, efficient and affordable approach to detect the students’ engagement levels in an e-learning environment by using webcams. Our method analyzes spatiotemporal features of e-learners’ micro body gestures, which will be mapped to emotions and appropriate engagement states. The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames. We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset. The adopted C3D model was used based on two different approaches; as a feature More >