Qaisar Abbas^1,*, Mostafa E. A. Ibrahim^1,2, Abdul Rauf Baig¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4573-4590, 2022, DOI:10.32604/cmc.2022.023670 - 14 January 2022

Abstract Diabetic retinopathy (DR) diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features. This task is very difficult for ophthalmologists and time-consuming. Therefore, many computer-aided diagnosis (CAD) systems were developed to automate this screening process of DR. In this paper, a CAD-DR system is proposed based on preprocessing and a pre-train transfer learning-based convolutional neural network (PCNN) to recognize the five stages of DR through retinal fundus images. To develop this CAD-DR system, a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related… More >

Wuqin Tang, Qiang Yang, Wenjun Yan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1423-1439, 2022, DOI:10.32604/cmes.2022.018313 - 30 December 2021

Abstract Defects detection with Electroluminescence (EL) image for photovoltaic (PV) module has become a standard test procedure during the process of production, installation, and operation of solar modules. There are some typical defects types, such as crack, finger interruption, that can be recognized with high accuracy. However, due to the complexity of EL images and the limitation of the dataset, it is hard to label all types of defects during the inspection process. The unknown or unlabeled create significant difficulties in the practical application of the automatic defects detection technique. To address the problem, we proposed… More >

Liwei Wang^1,2,3, Jiankun Sun^1,2,3, Xiong Luo^1,2,3,*, Xi Yang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1003-1016, 2022, DOI:10.32604/cmes.2022.018492 - 13 December 2021

Abstract With the development of information technology, malware threats to the industrial system have become an emergent issue, since various industrial infrastructures have been deeply integrated into our modern works and lives. To identify and classify new malware variants, different types of deep learning models have been widely explored recently. Generally, sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability. However, in current practical applications, an ample supply of data is absent in most specific industrial malware detection scenarios. Transfer learning as an effective approach can be used to More >

Amna Mir¹, Umer Yasin¹, Salman Naeem Khan¹, Atifa Athar^3,*, Riffat Jabeen², Sehrish Aslam¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3733-3746, 2022, DOI:10.32604/cmc.2022.022524 - 07 December 2021

Abstract Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes lesions on the retina that affect vision. Late detection of DR can lead to irreversible blindness. The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly. While, Classical Transfer learning models are extensively used for computer aided detection of DR; however, their maintenance costs limits detection performance rate. Therefore, Quantum Transfer learning is a better option to address this problem in an optimized manner. The significance of Hybrid quantum transfer learning approach includes that it performs heuristically.… More >

Maha Farouk S. Sabir¹, Irfan Mehmood^2,*, Wafaa Adnan Alsaggaf³, Enas Fawai Khairullah³, Samar Alhuraiji⁴, Ahmed S. Alghamdi⁵, Ahmed A. Abd El-Latif⁶

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4151-4166, 2022, DOI:10.32604/cmc.2022.017865 - 07 December 2021

Abstract Today, due to the pandemic of COVID-19 the entire world is facing a serious health crisis. According to the World Health Organization (WHO), people in public places should wear a face mask to control the rapid transmission of COVID-19. The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places. Therefore, it is very difficult to manually monitor people in overcrowded areas. This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places, by presenting an automated system that automatically… More >

Qaisar Abbas^1,*, Mostafa E.A. Ibrahim^1,2, Shakir Khan¹, Abdul Rauf Baig¹

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1999-2007, 2022, DOI:10.32604/cmc.2022.022553 - 03 November 2021

Abstract Traffic accidents are caused by driver fatigue or distraction in many cases. To prevent accidents, several low-cost hypovigilance (hypo-V) systems were developed in the past based on a multimodal-hybrid (physiological and behavioral) feature set. Similarly in this paper, real-time driver inattention and fatigue (Hypo-Driver) detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features. The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions. To get enhanced visual facial features in uncontrolled environment, three cameras are deployed on multiview points… More >

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 >