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Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current analytics and Comparisons

IT PROFESSIONAL 2021年第3期23卷 63-67页

作者： Rehman, Amjad Saba, Tanzila Tariq, Usman Ayesha, Noor CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj Saudi Arabia Zhengzhou Univ Sch Clin Med Zhengzhou Peoples R China

Currently, the world faces a novel coronavirus disease 2019 (COVID-19) challenge and infected cases are increasing exponentially. COVID-19 is a disease that has been reported by the WHO in March 2020, caused by a virus called the SARS-CoV-2. As of 10 March 2021, more than 150 million people were infected and 3v million died. Researchers strive to find out about the virus and recommend effective actions. An unprecedented increase in pathogens is happening and a major attempt is being made to tackle the epidemic. This article presents deep learning-based COVID-19 detection using CT and X-ray images and data analytics on its spread worldwide. This article's research structure builds on a recent analysis of the COVID-19 data and prospective research to systematize current resources, help the researchers, practitioners by using in-depth learning methodologies to build solutions for the COVID-19 pandemic.

关键词： COVID-19 Deep learning Pathogens Pandemics Computed tomography data science Market research

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IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction

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Computers, Materials & Continua 2022年第6期71卷 6219-6237页

作者： Tanzila Saba Rabia Javed Mohd Shafry Mohd Rahim Amjad Rehman Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Johor BahruMalaysia Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent *** is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel ***,detection and treatment at an early stage can result in a higher chance of *** classical methods of detection are expensive and ***,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of *** current improvement in computer-aided systems is providing very encouraging results in terms of precision and *** this article,we propose an improved region growing technique for efficient extraction of the lesion *** analysis and detection ofMelanoma are helpful for the expert *** CNN features are extracted using the pre-trained VGG-19 deep learning *** the end,the selected features are classified by *** proposed technique is gauged on openly accessible two datasets ISIC 2017 and *** the evaluation of our proposed framework,qualitative and quantitative experiments are *** suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 *** results are notably better than the results of prevalent methods available on the same *** machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of *** proposed method detects and classifies melanoma far better than other ***,our framework gained promising results in both segmentation and classification phases.

关键词： Deep features extraction lesion segmentation melanoma detection SVM VGG-19 healthcare IoMT public health

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Empowering Real-Time data Optimizing Framework Using artificial intelligence of Things for Sustainable Computing

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IEEE INTERNET OF THINGS JOURNAL 2024年第24期11卷 39094-39102页

作者： Haseeb, Khalid Rehman, Amjad Saba, Tanzila Wang, Huihui Alruwaili, Fahad F. CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 12435 Saudi Arabia Northeastern Univ Comp Programs Arlington VA 22209 USA Shaqra Univ Coll Comp & Informat Technol Dept Comp & Network Engn Shaqra 14254 Saudi Arabia

By exploring the future network, smart technologies promote the development of cutting-edge industrial applications. Internet of Things (IoT) systems use sensing approaches to acquire data and control real-time processing and complex tasks. Several techniques have been proposed for coping with environmental behavior in industrial management and reducing the response in crucial circumstances. However, due to the unique and limited constraints of the industrial environment, managing data routing and sustainable development are recent research concerns. In addition, security is essential for industrial communication systems due to the probability of unauthorized access, thus trust level must be improved. The framework addresses real-world challenges in industrial networks by incorporating a lightweight data verification algorithm designed for green communication, reducing energy consumption while maintaining data integrity. First, predictive computing is implemented using ant colony optimization (ACO) based on real-time requirements and selects the dynamic and communication channels for data transmission across the industrial platform. Second, mobile sinks offer more authentic techniques for verifying sensor data and delivering it securely to the cloud servers. The framework was evaluated and validated in a simulation-based environment, revealing a considerable improvement in terms of network throughput, packet drop ratio, connectivity ratio, and network overhead over the existing approaches.

关键词： Internet of Things Routing Reliability artificial intelligence Wireless sensor networks Real-time systems Wireless communication Ant colony optimization (ACO) complex networks information security sustainable applications technological development

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Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network

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MICROSCOPY RESEARCH AND TECHNIQUE 2022年第1期85卷 385-397页

作者： Amin, Javeria Anjum, Muhammad Almas Sharif, Muhammad Rehman, Amjad Saba, Tanzila Zahra, Rida Univ Wah Dept Comp Sci Wah Cantt Pakistan Natl Univ Technol NUTECH Islamabad Pakistan COMSATS Univ Islamabad Dept Comp Sci Wah Campus Wah Cantt Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.

关键词： Deeplabv3 denoise convolutional neural network (DnCNN) healthcare public health ResNet-18 stack sparse autoencoder deep learning model (SSAE)

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Mobile Application Usability Evaluation: A Study Based on Demography

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IEEE ACCESS 2022年 10卷 41512-41524页

作者： Ali, Wahaj Riaz, Omer Mumtaz, Shahzad Khan, Amjad Rehman Saba, Tanzila Bahaj, Saeed Ali Islamia Univ Bahawalpur Fac Comp Bahawalpur 63100 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia

An application or product is considered "usable" if it is pleasing, easy to use, and works as expected user interface. Most companies majorly focus on the application's functional requirements but put minimal effort into user experience (usability). Consumers' adaptation of these applications depends on the number of features and user interface. In this work, an android based application, "Houzcalls" is used for usability study using PACMAD usability model. This work is focused on the variations in PACMAD attributes based on the participants' education and age. Participants are segregated into two major groups FG1 and FG2 based on their education. All the participants with more than 10 years of education are in FG1 while others in FG2. Each focal group is divided into four subgroups Under 25, 25-35, 36-45, and Over 45 based on their ages. The results have shown participants in FG1 have shown more Effectiveness, Efficiency, Satisfaction, Learnability, and Memorability. In contrast, they have committed fewer Errors and shown less Cognitive Load during usability testing as compared to FG2. These variations can also be seen age-wise as generally "Under 25 and 25-35" subgroups have shown better results than other subgroups. It is inferred from the study that application usability and acceptability can be increased by considering the general population during development which includes all groups of people based on education and age.

关键词： Usability Testing Education Load modeling Software ISO Standards Mobile applications HCI usability testing attribute-based evaluation PACMAD usability model UI test design test execution focus groups age education mobile application requirement engineering

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Secure and Energy-Aware Heuristic Routing Protocol for Wireless Sensor Network

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IEEE ACCESS 2020年 8卷 163962-163974页

作者： Haseeb, Khalid Almustafa, Khaled Mohamad Jan, Zahoor Saba, Tanzila Tariq, Usman Islamia Coll Peshawar Dept Comp Sci Peshawar 25000 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA CCIS Riyadh 12435 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj 16278 Saudi Arabia

Wireless Sensor Networks (WSNs) achieve much attention from various domains because of its easy maintenance, self-configuration, and scalability characteristics. It is comprised of small-sized sensors that interact with the Internet of Things (IoT) for observing and recording the physical conditions. The sensor nodes are autonomous and construct inter-communication topology with each other in an ad-hoc manner. However, the main restrictions of sensor nodes are their finite resources for energy management, data storage, transmission, and processing power. Different solutions have been addressed by researchers to overcome network performance due to bounded limitations of such battery-powered nodes, however, equalize the energy consumption and maintain the network throughput are the main research problems. Furthermore, due to the compromised nodes, the data is more prone to security vulnerabilities. Therefore, their security over the unpredictable network is other research concerns. Thus, the aim of this research article to propose a secure and energy-aware heuristic-based routing (SEHR) protocol for WSN to detect and prevent compromising data with efficient performance. Firstly, the proposed protocol makes use of an artificial intelligence-based heuristic analysis to accomplish a reliable, and intellectual learning scheme. Secondly, it protects the transmissions against adversary groups to attain security with the least complexity. Moreover, the route maintenance strategy is also achieved by using traffic exploration to reduce link failures and network dis-connectivity. The simulation results demonstrated the SEHR protocol improves the efficacy for network throughput by an average of 18%, packet drop ratio by 42%, end-to-end delay by 26%, energy consumption by 36%, faulty routes by 38%, network overhead by 44%, and computational overhead by 43% in dynamic scenarios as compared to existing work.

关键词： Routing Wireless sensor networks Sensors Routing protocols Energy efficiency Security artificial intelligence data privacy energy efficiency heuristic analysis wireless sensor network

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Properties of the Geometry of Solutions and Capacity of Multilayer Neural Networks with Rectified Linear Unit Activations

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Physical Review Letters 2019年第17期123卷 170602-170602页

作者： Carlo Baldassi Enrico M. Malatesta Riccardo Zecchina Artificial Intelligence Lab Institute for Data Science and Analytics Bocconi University Milano 20135 Italy

Rectified linear units (ReLUs) have become the main model for the neural units in current deep learning systems. This choice was originally suggested as a way to compensate for the so-called vanishing gradient problem which can undercut stochastic gradient descent learning in networks composed of multiple layers. Here we provide analytical results on the effects of ReLUs on the capacity and on the geometrical landscape of the solution space in two-layer neural networks with either binary or real-valued weights. We study the problem of storing an extensive number of random patterns and find that, quite unexpectedly, the capacity of the network remains finite as the number of neurons in the hidden layer increases, at odds with the case of threshold units in which the capacity diverges. Possibly more important, a large deviation approach allows us to find that the geometrical landscape of the solution space has a peculiar structure: While the majority of solutions are close in distance but still isolated, there exist rare regions of solutions which are much more dense than the similar ones in the case of threshold units. These solutions are robust to perturbations of the weights and can tolerate large perturbations of the inputs. The analytical results are corroborated by numerical findings.

关键词： artificial neural networks Disordered systems

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Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Mahmood, Tariq Saba, Tanzila Rehman, Amjad Alamri, Faten S. CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Facutly Informat Sci Vehari Campus Lahore 61100 Pakistan Princess Nourah bint Abdulrahman Univ Coll Sci Math Sci Dept Riyadh 84428 Saudi Arabia

Breast cancer, with its high mortality, faces diagnostic challenges due to variability in mammography quality and breast densities, leading to inconsistencies in radiological interpretations. Computer-aided diagnostic (CAD) systems, while helpful, struggle with accurately interpreting lesion characteristics such as morphology, density, and size. To address this, our study developed advanced deep-learning algorithms to improve the detection, localization, risk assessment, and classification of breast lesions, aiming to reduce false positives on human intervention and tackle slow convergence rates. Key innovations of the approach include preprocessing techniques, advanced filtering, and data augmentation strategies to optimize model performance, mitigating overand under-fitting concerns. A significant development is the Chaotic Leader Selective Filler Swarm Optimization (cLSFSO) method, which effectively detects breast-dense lesions by extracting textural and statistical features. Additionally, the study adapted deep learning models like modified VGGNet and SE-ResNet152 through transfer learning, significantly enhancing their capability to distinguish between normal and suspicious mammography regions. The study also introduces hybrid deep neural network-based approaches, including CNN+LSTM and CNN+SVM, for diagnosing and grading cancerous polyps from the pre -segmented ROIs. Besides, the transfer learning paradigm is employed to boost the efficacy in classifying breast masses and reducing computing time by modifying the final layer of the proposed pre-trained models. The integration of Grad-CAM techniques further refines our analysis, leading to more accurate assessments and improved diagnosis of breast anomalies. Evaluated using benchmark and private datasets, our algorithms demonstrated a sensitivity of 0.99 and an overall AUC of 0.99, indicating significant improvements in mammogram analysis. These advancements aid radiologists, potentially improving patient outcom

关键词： Breast carcinoma Deep neural networks Mammographic image Transfer learning Radiomic analysis Health issue

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Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering

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MICROSCOPY RESEARCH AND TECHNIQUE 2022年第1期85卷 339-351页

作者： Nawaz, Marriam Mehmood, Zahid Nazir, Tahira Naqvi, Rizwan Ali Rehman, Amjad Iqbal, Munwar Saba, Tanzila Univ Engn & Technol Dept Comp Sci Taxila Pakistan Univ Engn & Technol Dept Comp Engn Taxila Pakistan Sejong Univ Dept Unmanned Vehicle Engn Seoul South Korea Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh Saudi Arabia

Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively;however, the automated localization and segmentation of skin lesion at earlier stages is still a challenging task due to the low contrast between melanoma moles and skin portion and a higher level of color similarity between melanoma-affected and -nonaffected areas. In this paper, we present a fully automated method for segmenting the skin melanoma at its earliest stage by employing a deep-learning-based approach, namely faster region-based convolutional neural networks (RCNN) along with fuzzy k-means clustering (FKM). Several clinical images are utilized to test the presented method so that it may help the dermatologist in diagnosing this life-threatening disease at its earliest stage. The presented method first preprocesses the dataset images to remove the noise and illumination problems and enhance the visual information before applying the faster-RCNN to obtain the feature vector of fixed length. After that, FKM has been employed to segment the melanoma-affected portion of skin with variable size and boundaries. The performance of the presented method is evaluated on the three standard datasets, namely ISBI-2016, ISIC-2017, and PH2, and the results show that the presented method outperforms the state-of-the-art approaches. The presented method attains an average accuracy of 95.40, 93.1, and 95.6% on the ISIC-2016, ISIC-2017, and PH2 datasets, respectively, which is showing its robustness to skin lesion recognition and segmentation.

关键词： deep learning faster-RCNN fuzzy c-means clustering melanoma skin cancer

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