1
|
Shahzadi T, Ali MU, Majeed F, Sana MU, Diaz RM, Samad MA, Ashraf I. Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN. Diagnostics (Basel) 2023; 13:2975. [PMID: 37761342 PMCID: PMC10529899 DOI: 10.3390/diagnostics13182975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/14/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient's lumbar spine. Currently, the versatility and accuracy of LSS segmentation algorithms are limited. The objective of this research is to use magnetic resonance imaging (MRI) to automatically categorize LSS. This study presents a convolutional neural network (CNN)-based method to detect LSS using MRI images. Radiological grading is performed on a publicly available dataset. Four regions of interest (ROIs) are determined to diagnose LSS with normal, mild, moderate, and severe gradings. The experiments are performed on 1545 axial-view MRI images. Furthermore, two datasets-multi-ROI and single-ROI-are created. For training and testing, an 80:20 ratio of randomly selected labeled datasets is used, with fivefold cross-validation. The results of the proposed model reveal a 97.01% accuracy for multi-ROI and 97.71% accuracy for single-ROI. The proposed computer-aided diagnosis approach can significantly improve diagnostic accuracy in everyday clinical workflows to assist medical experts in decision making. The proposed CNN-based MRI image segmentation approach shows its efficacy on a variety of datasets. Results are compared to existing state-of-the-art studies, indicating the superior performance of the proposed approach.
Collapse
Affiliation(s)
- Turrnum Shahzadi
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (T.S.); (F.M.); (M.U.S.)
| | - Muhammad Usman Ali
- Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan;
| | - Fiaz Majeed
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (T.S.); (F.M.); (M.U.S.)
| | - Muhammad Usman Sana
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (T.S.); (F.M.); (M.U.S.)
| | - Raquel Martínez Diaz
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain;
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Universidad Internacional do Cuanza, Cuito EN250, Bié, Angola
| | - Md Abdus Samad
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| |
Collapse
|
2
|
Shahzadi S, Butt NA, Sana MU, Pascual IE, Urbano MB, Díez IDLT, Ashraf I. Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer's Disease Using Machine Learning Approaches. Diagnostics (Basel) 2023; 13:2871. [PMID: 37761238 PMCID: PMC10527683 DOI: 10.3390/diagnostics13182871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/25/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
This study sought to investigate how different brain regions are affected by Alzheimer's disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer's disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer's disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each.
Collapse
Affiliation(s)
- Samra Shahzadi
- Department of Computer Science, Faculty of Computing and Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (S.S.); (N.A.B.)
| | - Naveed Anwer Butt
- Department of Computer Science, Faculty of Computing and Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (S.S.); (N.A.B.)
| | - Muhammad Usman Sana
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan;
| | - Iñaki Elío Pascual
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (I.E.P.); (M.B.U.)
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
- Fundación Universitaria Internacional de Colombia, Bogotá 11001, Colombia
| | - Mercedes Briones Urbano
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (I.E.P.); (M.B.U.)
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
| | - Isabel de la Torre Díez
- Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| |
Collapse
|
3
|
Umer M, Sadiq S, karamti H, Abdulmajid Eshmawi A, Nappi M, Usman Sana M, Ashraf I. ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification. Pattern Recognit Lett 2022; 164:224-231. [PMID: 36407854 PMCID: PMC9664766 DOI: 10.1016/j.patrec.2022.11.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/09/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022]
Abstract
Pandemics influence people negatively and people experience fear and disappointment. With the global outspread of COVID-19, the sentiments of the general public are substantially influenced, and analyzing their sentiments could help to devise corresponding policies to alleviate negative sentiments. Often the data collected from social media platforms is unstructured leading to low classification accuracy. This study brings forward an ensemble model where the benefits of handcrafted features and automatic feature extraction are combined by machine learning and deep learning models. Unstructured data is obtained, preprocessed, and annotated using TextBlob and VADER before training machine learning models. Similarly, the efficiency of Word2Vec, TF, and TF-IDF features is also analyzed. Results reveal the better performance of the extra tree classifier when trained with TF-IDF features from TextBlob annotated data. Overall, machine learning models perform better with TF-IDF and TextBlob. The proposed model obtains superior performance using both annotation techniques with 0.97 and 0.95 scores of accuracy using TextBlob and VADER respectively with Word2Vec features. Results reveal that use of machine learning and deep learning models together with a voting criterion tends to yield better results than other machine learning models. Analysis of sentiments indicates that predominantly people possess negative sentiments regarding COVID-19.
Collapse
Affiliation(s)
- Muhammad Umer
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
| | - Saima Sadiq
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Hanen karamti
- Department of computer sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia
| | | | - Michele Nappi
- Department of Computer Science, University of Salerno, Fisciano, Italy,Corresponding author
| | - Muhammad Usman Sana
- College of Computer Science Technology, Xian University of Science and Technology, Xian, Shaanxi 710054, China
| | - Imran Ashraf
- Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea,Corresponding author
| |
Collapse
|
4
|
Mir TS, Liaqat HB, Kiren T, Sana MU, Alvarez RM, Miró Y, Pascual Barrera AE, Ashraf I. Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing. Sensors (Basel) 2022; 22:8778. [PMID: 36433374 PMCID: PMC9696224 DOI: 10.3390/s22228778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/09/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected.
Collapse
Affiliation(s)
- Tahira Sarwar Mir
- Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan
| | - Hannan Bin Liaqat
- Department of Information Sciences, University of Education, Lahore 41000, Pakistan
| | - Tayybah Kiren
- Department of Computer Science (RCET), University of Engineering and Technology, Lahore 54890, Pakistan
| | - Muhammad Usman Sana
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan
| | - Roberto Marcelo Alvarez
- Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Project Management, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Department of Project Management, Universidade Internacional do Cuanza, Cuito, Bié, Angola
| | - Yini Miró
- Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Department of Project Management, Universidade Internacional do Cuanza, Cuito, Bié, Angola
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
| | - Alina Eugenia Pascual Barrera
- Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Fundación Universitaria Internacional de Colombia, Bogotá 111311, Colombia
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
| |
Collapse
|
5
|
Javaid F, Wang A, Sana MU, Husain A, Ashraf I. An Optimized Approach to Channel Modeling and Impact of Deteriorating Factors on Wireless Communication in Underground Mines. Sensors (Basel) 2021; 21:s21175905. [PMID: 34502794 PMCID: PMC8433738 DOI: 10.3390/s21175905] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/20/2021] [Accepted: 08/28/2021] [Indexed: 11/23/2022]
Abstract
The environment of underground coal mines has challenging properties that makes this zone inadaptable for a stable communication system. Additionally, various deteriorating physical parameters strongly affect the performance of wireless networks, which leads to limited network coverage and poor quality of data communication. This study investigates the communication capability in underground coal mines by optimizing the wireless link to develop a stable network for an underground hazardous environment. A hybrid channel-modeling scheme is proposed to characterize the environment of underground mines for wireless communication by classifying the area of a mine into the main gallery and sub-galleries. The complex segments of mine are evaluated by categorizing the wireless links for the line-of-sight (LOS) zones and hybrid modeling is employed to examine the characteristics of electromagnetic signal propagation. For hybrid channel modeling, the multimode waveguide model and geometrical optic (GO) model are used for developing an optimal framework that improves the accessibility of the network in the critical time-varying environment of mines. Moreover, the influence of various deteriorating factors is analyzed using 2.4 GHz to 5 GHz frequency band to study its relationship with the vital constraints of an underground mine. The critical factors such as path loss, roughness loss, delay spread, and shadow fading are examined under detailed analysis with variation in link structure for the mine.
Collapse
Affiliation(s)
- Fawad Javaid
- College of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; (F.J.); (A.H.)
| | - Anyi Wang
- College of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; (F.J.); (A.H.)
- Correspondence: (A.W.); (I.A.)
| | - Muhammad Usman Sana
- College of Computer Science Technology, Xi’an University of Science and Technology, Xi’an 710054, China;
| | - Asif Husain
- College of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; (F.J.); (A.H.)
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38544, Korea
- Correspondence: (A.W.); (I.A.)
| |
Collapse
|
6
|
Usman Sana M, Li Z. Efficiency aware scheduling techniques in cloud computing: a descriptive literature review. PeerJ Comput Sci 2021; 7:e509. [PMID: 34013035 PMCID: PMC8114819 DOI: 10.7717/peerj-cs.509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/05/2021] [Indexed: 06/12/2023]
Abstract
In the last decade, cloud computing becomes the most demanding platform to resolve issues and manage requests across the Internet. Cloud computing takes along terrific opportunities to run cost-effective scientific workflows without the requirement of possessing any set-up for customers. It makes available virtually unlimited resources that can be attained, organized, and used as required. Resource scheduling plays a fundamental role in the well-organized allocation of resources to every task in the cloud environment. However along with these gains many challenges are required to be considered to propose an efficient scheduling algorithm. An efficient Scheduling algorithm must enhance the implementation of goals like scheduling cost, load balancing, makespan time, security awareness, energy consumption, reliability, service level agreement maintenance, etc. To achieve the aforementioned goals many state-of-the-art scheduling techniques have been proposed based upon hybrid, heuristic, and meta-heuristic approaches. This work reviewed existing algorithms from the perspective of the scheduling objective and strategies. We conduct a comparative analysis of existing strategies along with the outcomes they provide. We highlight the drawbacks for insight into further research and open challenges. The findings aid researchers by providing a roadmap to propose efficient scheduling algorithms.
Collapse
|