Mohammad Yamin^1,*, Adnan Ahmed Abi Sen², Zenah Mahmoud AlKubaisy¹, Rahaf Almarzouki¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2283-2298, 2021, DOI:10.32604/cmc.2021.017433

Abstract COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the… More >

Hanan Abdullah Mengash^1,*, Hanan A. Hosni Mahmoud^2,3

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1551-1563, 2021, DOI:10.32604/cmc.2021.016907

Abstract Detection of brain tumors in MRI images is the first step in brain cancer diagnosis. The accuracy of the diagnosis depends highly on the expertise of radiologists. Therefore, automated diagnosis of brain cancer from MRI is receiving a large amount of attention. Also, MRI tumor detection is usually followed by a biopsy (an invasive procedure), which is a medical procedure for brain tumor classification. It is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive procedures. Convolutional neural network (CNN) is deemed to be one of the best machine learning… More >

Javaria Amin¹, Muhammad Almas Anjum², Muhammad Sharif³, Seifedine Kadry⁴, Yunyoung Nam^5,*, ShuiHua Wang⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2693-2709, 2021, DOI:10.32604/cmc.2021.016871

Abstract Recognition of human gait is a difficult assignment, particularly for unobtrusive surveillance in a video and human identification from a large distance. Therefore, a method is proposed for the classification and recognition of different types of human gait. The proposed approach is consisting of two phases. In phase I, the new model is proposed named convolutional bidirectional long short-term memory (Conv-BiLSTM) to classify the video frames of human gait. In this model, features are derived through convolutional neural network (CNN) named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information. In phase II,… More >

Tapan Kumar Das¹, Pradeep Kumar Roy², Mohy Uddin³, Kathiravan Srinivasan¹, Chuan-Yu Chang^4,*, Shabbir Syed-Abdul⁵

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2413-2429, 2021, DOI:10.32604/cmc.2021.016698

Abstract Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for abnormality detection over MRI data… More >

Amany M. Sarhan¹, Nada M. Elshennawy¹, Dina M. Ibrahim^1,2,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1531-1549, 2021, DOI:10.32604/cmc.2021.016509

Abstract

Lip reading is typically regarded as visually interpreting the speaker’s lip movements during the speaking. This is a task of decoding the text from the speaker’s mouth movement. This paper proposes a lip-reading model that helps deaf people and persons with hearing problems to understand a speaker by capturing a video of the speaker and inputting it into the proposed model to obtain the corresponding subtitles. Using deep learning technologies makes it easier for users to extract a large number of different features, which can then be converted to probabilities of letters to obtain accurate results. Recently proposed methods for… More >

Muhammed Binsawad^1,*, Marwan Albahar², Abdullah Bin Sawad¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2791-2806, 2021, DOI:10.32604/cmc.2021.016141

Abstract The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on the health and welfare of the global population. A key measure to combat COVID-19 has been the effective screening of infected patients. A vital screening process is the chest radiograph. Initial studies have shown irregularities in the chest radiographs of COVID-19 patients. The use of the chest X-ray (CXR), a leading diagnostic technique, has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases. This study introduces a dilated bi-branched convoluted neural network (CNN)… More >

Bakhtawar Aslam¹, Ziyad Awadh Alrowaili², Bushra Khaliq¹, Jaweria Manzoor¹, Saira Raqeeb¹, Fahad Ahmad^3,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2159-2178, 2021, DOI:10.32604/cmc.2021.015922

Abstract The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially. This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network (F-RCNN). The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions. Furthermore, image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation. The permanent changes in… More >

Debajit Datta¹, Pramod Kumar Maurya¹, Kathiravan Srinivasan², Chuan-Yu Chang^3,*, Rishav Agarwal¹, Ishita Tuteja¹, V. Bhavyashri Vedula¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2545-2561, 2021, DOI:10.32604/cmc.2021.015478

Abstract The pandemic situation in 2020 brought about a ‘digitized new normal’ and created various issues within the current education systems. One of the issues is the monitoring of students during online examination situations. A system to determine the student’s eye gazes during an examination can help to eradicate malpractices. In this work, we track the users’ eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier. We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network (CNN) models, namely the AlexNet model and the… More >

Sidharth Samanta¹, Mrutyunjaya Panda¹, Somula Ramasubbareddy², S. Sankar³, Daniel Burgos^4,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1937-1948, 2021, DOI:10.32604/cmc.2021.014406

Abstract Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) object detection framework to make… More >

Yang Wu¹, Lihong Xu^1,*, Erik D. Goodman²

Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 561-576, 2021, DOI:10.32604/iasc.2021.016415

Abstract Deep convolutional neural network (DCNN) requires a lot of data for training, but there has always been data vacuum in agriculture, making it difficult to label all existing data accurately. Therefore, a lightweight tomato leaf disease identification network supported by Variational auto-Encoder (VAE) is proposed to improve the accuracy of crop leaf disease identification. In the lightweight network, multi-scale convolution can expand the network width, enrich the extracted features, and reduce model parameters such as deep separable convolution. VAE makes full use of a large amount of unlabeled data to achieve unsupervised learning, and then uses labeled data for supervised… More >