Automated schizophrenia detection model using blood sample scattergram images and local binary pattern
Article
Tasci, Burak, Tasci, Gulay, Ayyildiz, Hakan, Kamath, Aditya P., Barua, Prabal Datta, Tuncer, Turker, Dogan, Sengul, Ciaccio, Edward J., Chakraborty, Subrata . and Acharya, U. Rajendra. 2024. "Automated schizophrenia detection model using blood sample scattergram images and local binary pattern." Multimedia Tools and Applications. 83 (14), p. 42735–42763. https://doi.org/10.1007/s11042-023-16676-0
Article Title | Automated schizophrenia detection model using blood sample scattergram images and local binary pattern |
---|---|
ERA Journal ID | 18083 |
Article Category | Article |
Authors | Tasci, Burak, Tasci, Gulay, Ayyildiz, Hakan, Kamath, Aditya P., Barua, Prabal Datta, Tuncer, Turker, Dogan, Sengul, Ciaccio, Edward J., Chakraborty, Subrata . and Acharya, U. Rajendra |
Journal Title | Multimedia Tools and Applications |
Journal Citation | 83 (14), p. 42735–42763 |
Number of Pages | 29 |
Year | 2024 |
Publisher | Springer |
Place of Publication | United States |
ISSN | 1380-7501 |
1573-7721 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11042-023-16676-0 |
Web Address (URL) | https://link.springer.com/article/10.1007/s11042-023-16676-0 |
Abstract | The main goal of this paper is to advance the field of automated Schizophrenia (SZ) detection methods by presenting a pioneering feature engineering technique that achieves high classification accuracy while maintaining low time complexity. Furthermore, we introduce a novel data type known as scattergram images, which can be obtained through a simple blood test. These scattergram images provide a cost-effective approach for SZ detection. The scattergram image datasets used in this research consist of images collected from 202 participants, with 106 individuals diagnosed with SZ and the remaining 96 individuals serving as control subjects. Our objective is to assess the ability of scattergram images to detect SZ. To achieve accurate classification with minimal computational burden, we propose a feature engineering model based on the local binary pattern (LBP) technique. Initially, a preprocessing method is applied to separate blood cells from the scattergram images, followed by image rotation to ensure robust results. Both 1D-LBP and 2D-LBP are utilized to extract informative features. Our feature engineering model incorporates iterative neighborhood component analysis (INCA) to select the most relevant features. In the classification phase, shallow classifiers are employed to demonstrate the capability of the extracted features for classification. Information fusion is accomplished using iterative hard majority voting (IHMV) to select the most accurate result. We have tested our proposal on the collected two scattergram image datasets and our proposal attained 89.29% and 90.58% classification accuracies on the used datasets, respectively. The findings of this study demonstrate the potential of scattergram images as an effective tool for SZ detection, thus serving as a promising new biomarker in the field. Our auto-detection model of SZ disease is clinically ready for use in hospital settings and outpatient clinics as an additional means to assist clinicians in their diagnostics procedure. |
Keywords | Schizophrenia detection; Scattergram images; Local Binary Pattern; Iterative Neighborhood Component Analysis; Iterative Hard Majority Voting |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Firat University, Turkey |
Elazig Fethi Sekin City Hospital, Turkey | |
Brown University, United States | |
School of Business | |
Columbia University Irving Medical Center, United States | |
University of New England | |
University of Technology Sydney | |
School of Mathematics, Physics and Computing |
Permalink -
https://research.usq.edu.au/item/z297w/automated-schizophrenia-detection-model-using-blood-sample-scattergram-images-and-local-binary-pattern
114
total views0
total downloads7
views this month0
downloads this month
Export as
Related outputs
Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images
Key, Sefa, Kurum, Huseyin, Esmez, Omer, Hafeez-Baig, Abdul, Hajiyeva, Rena, Dogan, Sengul and Tuncer, Turker. 2025. "Automated hip dysplasia detection using novel FlexiLBPHOG model with ultrasound images." Ain Shams Engineering Journal. 16 (1). https://doi.org/10.1016/j.asej.2024.103235Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023)
Atmakuru, Anirudh, Shahini, Alen, Chakraborty, Subrata, Seoni, Silvia, Salvi, Massimo, Hafeez-Baig, Abdul, Rashid, Sadaf, Tan, Ru San, Barua, Prabal Datta, Molinari, Filippo and Acharya, U Rajendra. 2025. "Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023)." Information Fusion. 114. https://doi.org/10.1016/j.inffus.2024.102673Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts
Ghimire, Sujan, AL-Musaylh, Mohanad S., Nguyen-Huy, Thong, Deo, Ravinesh C., Acharya, Rajendra, Casillas-Perez, David, Yaseen, Zaher Mundher and Salcedo-sanz, Sancho. 2025. "Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts." Applied Energy. 378 (Part A). https://doi.org/10.1016/j.apenergy.2024.124763Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification
Tuncer, Turker, Dogan, Sengul, Baygin, Mehmet, Tasci, Irem, Mungen, Bulent, Tasci, Burak, Barua, Prabal Datta and Acharya, U.R.. 2024. "Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification." Knowledge-Based Systems. 305. https://doi.org/10.1016/j.knosys.2024.112555Retinal Health Screening Using Artificial Intelligence with Digital Fundus Images: A Review of the Last Decade (2012-2023)
Deo, Ravinesh C., Islam, Saad, Barua, Prabal Datta, Soar, Jeffrey, Yu, Ping and Acharya, U. Rajendra. 2024. "Retinal Health Screening Using Artificial Intelligence with Digital Fundus Images: A Review of the Last Decade (2012-2023)." IEEE Access. 12, pp. 176630-176685. https://doi.org/10.1109/ACCESS.2024.3477420Automated EEG-based language detection using directed quantum pattern technique
Dogan, Sengul, Tuncer, Turker, Barua, Prabal Datta and Acharya, U.R.. 2024. "Automated EEG-based language detection using directed quantum pattern technique." Applied Soft Computing. 167 (Part A). https://doi.org/10.1016/j.asoc.2024.112301A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images
Katar, Oguzhan, Yildirim, Ozal, Tan, Ru-San and Acharya, U Rajendra. 2024. "A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images." Diagnostics. 14 (22). https://doi.org/10.3390/diagnostics14222497Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies
Akpinar, Muhammed Halil, Sengur, Abdulkadir, Salvi, Massimo, Seoni, Silvia, Faust, Oliver, Mir, Hasan, Molinari,Filippo and Acharya, U. Rajendra. 2024. "Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies." IEEE Open Journal of Engineering in Medicine and Biology. 6, pp. 183-192. https://doi.org/10.1109/OJEMB.2024.3508472RECOMED: A comprehensive pharmaceutical recommendation system
Zomorodi, Mariam, Ghodsollahee, Ismail, Martin, Jennifer H, Talley, Nicholas J, Salari, Vahid, Pławiak, Paweł, Rahimi, Kazem and Acharya, U.R.. 2024. "RECOMED: A comprehensive pharmaceutical recommendation system." Artificial Intelligence in Medicine. 157. https://doi.org/10.1016/j.artmed.2024.102981Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade
Abdollahi, Mirsaeed, Jafarizadeh, Ali, Ghafouri-Asbagh, Amirhosein, Sobhi, Navid, Pourmoghtader, Keysan, Pedrammehr, Siamak, Asadi, Houshyar, Tan, Ru-San, Alizadehsani, Roohallah and Acharya, U. Rajendra. 2024. "Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade." WIREs Data Mining and Knowledge Discovery. 14 (6). https://doi.org/10.1002/widm.1560Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models
Telangore, Hardik, Azad, Victor, Sharma, Manish, Bhurane, Ankit, Tan, Ru San and Acharya, U. Rajendra. 2024. "Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models." Computer Methods and Programs in Biomedicine. 257. https://doi.org/10.1016/j.cmpb.2024.108455A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization
Hardalac, Firat, Akmal, Haad, Ayturan, Kubilay, Acharya, U. Rajendra and Tan, Ru-San. 2024. "A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization." Interdisciplinary Sciences: Computational Life Sciences. 16 (4), pp. 882-906. https://doi.org/10.1007/s12539-024-00647-6Automated System for the Detection of Heart Anomalies Using Phonocardiograms: A Systematic Review
Gudigar, Anjan, Raghavendra, U., Maithri, M., Samanth, Jyothi, Inamdar, Mahesh Anil, Vidhya, V., Vicnesh, Jahmunah, Prabhu, Mukund A., Tan, Ru-San, Yeong, Chai Hong, Molinari, Filippo and Acharya, U. R.. 2024. "Automated System for the Detection of Heart Anomalies Using Phonocardiograms: A Systematic Review." IEEE Access. 12, pp. 138399-138428. https://doi.org/10.1109/ACCESS.2024.3465511Exploring the perspectives of Australian primary school teachers on students learning about project management
Delle-Vergini, Sante, Eacersall, Douglas, Dann, Chris, Ally, Mustafa and Chakraborty, Subrata. 2024. "Exploring the perspectives of Australian primary school teachers on students learning about project management." Issues in Educational Research. 34 (3), pp. 928-952.