1
|
Saidani O, Umer M, Alturki N, Alshardan A, Kiran M, Alsubai S, Kim TH, Ashraf I. White blood cells classification using multi-fold pre-processing and optimized CNN model. Sci Rep 2024; 14:3570. [PMID: 38347011 PMCID: PMC10861568 DOI: 10.1038/s41598-024-52880-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 01/24/2024] [Indexed: 02/15/2024] Open
Abstract
White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.
Collapse
Affiliation(s)
- Oumaima Saidani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Muhammad Umer
- Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
| | - Nazik Alturki
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Amal Alshardan
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Muniba Kiran
- Department of Biotechnology, Virtual University of Pakistan, M.A. Jinnah Campus, Defence Road, Off Raiwind Road, Lahore, 54000, Pakistan
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, 11942, Al-Kharj, Saudi Arabia
| | - Tai-Hoon Kim
- School of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, 50, Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea.
| | - Imran Ashraf
- Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
| |
Collapse
|
2
|
Kumar S, Sharma B, Singh V. A multiscale modeling framework to study the interdependence of brain, behavior, and pandemic. NONLINEAR DYNAMICS 2023; 111:7729-7749. [PMID: 36710874 PMCID: PMC9857926 DOI: 10.1007/s11071-022-08204-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 12/17/2022] [Indexed: 06/18/2023]
Abstract
A major constraint of the behavioral epidemiological models is the assumption that human behavior is static; however, it is highly dynamic, especially in uncertain circumstances during a pandemic. To incorporate the dynamicity of human nature in the existing epidemiological models, we propose a population-wide multi-time-scale theoretical framework that assimilates neuronal plasticity as the basis of altering human emotions and behavior. For that, variable connection weights between different brain regions and their firing frequencies are coupled with a compartmental susceptible-infected-recovered model to incorporate the intrinsic dynamicity in the contact transmission rate ( β ). As an illustration, a model of fear conditioning in conjunction with awareness campaigns is developed and simulated. Results indicate that in the presence of fear conditioning, there exists an optimum duration of daily broadcast time during which awareness campaigns are most effective in mitigating the pandemic. Further, global sensitivity analysis using the Morris method highlighted that the learning rate and firing frequency of the unconditioned circuit are crucial regulators in modulating the emergent pandemic waves. The present study makes a case for incorporating neuronal dynamics as a basis of behavioral immune response and has further implications in designing awareness campaigns.
Collapse
Affiliation(s)
- Spandan Kumar
- School of Social Sciences, Indira Gandhi National Open University, New Delhi, 110068 India
- National Institute of Public Cooperation and Child Development, New Delhi, 110016 India
| | - Bhanu Sharma
- Department of Biophysics, South Campus, University of Delhi, New Delhi, 110021 India
| | - Vikram Singh
- Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Kangra, Himachal Pradesh 176215 India
| |
Collapse
|
3
|
Khan SW, Hafeez Q, Khalid MI, Alroobaea R, Hussain S, Iqbal J, Almotiri J, Ullah SS. Anomaly Detection in Traffic Surveillance Videos Using Deep Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:6563. [PMID: 36081022 PMCID: PMC9460365 DOI: 10.3390/s22176563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 08/21/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.
Collapse
Affiliation(s)
- Sardar Waqar Khan
- Department of Information Technology, University of Sialkot, Sialkot 51040, Pakistan
| | - Qasim Hafeez
- School of Physic Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Muhammad Irfan Khalid
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, SA, Italy
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Saddam Hussain
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan
| | - Jawaid Iqbal
- School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
| | - Jasem Almotiri
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Syed Sajid Ullah
- Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway
- Department of Electrical and Computer Engineering, Villanova University, Villanova, PA 19085, USA
| |
Collapse
|
4
|
Khurana Batra P, Aggarwal P, Wadhwa D, Gulati M. Predicting pattern of coronavirus using X-ray and CT scan images. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022; 11:39. [PMID: 36212780 PMCID: PMC9532815 DOI: 10.1007/s13721-022-00382-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 08/06/2022] [Accepted: 09/21/2022] [Indexed: 11/03/2022]
Abstract
Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world's central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy.
Collapse
Affiliation(s)
- Payal Khurana Batra
- grid.419639.00000 0004 1772 7740Department of CSE and IT, Jaypee Institute of Information Technology, Noida, India
| | - Paras Aggarwal
- grid.419639.00000 0004 1772 7740Department of CSE and IT, Jaypee Institute of Information Technology, Noida, India
| | - Dheeraj Wadhwa
- grid.419639.00000 0004 1772 7740Department of CSE and IT, Jaypee Institute of Information Technology, Noida, India
| | - Mehul Gulati
- grid.419639.00000 0004 1772 7740Department of CSE and IT, Jaypee Institute of Information Technology, Noida, India
| |
Collapse
|