Bleuler E. English translation: textbook of psychiatry. Berlin: Springer Verlag; 1920.
Jablensky A. The diagnostic concept of schizophrenia: its history, evolution, and future prospects. Dialogues Clin Neurosci. 2010;12(3):271–87. https://doi.org/10.31887/DCNS.2010.12.3/ajablensky.
Ruiz P, Molina E, Aguirre-Loaiza H, Daza MT. Positive symptoms of schizophrenia and their relationship with cognitive and emotional executive functions. Cognitive Res: Princ Implic. 2022. https://doi.org/10.1186/s41235-022-00428-z.
Correll CU, Schooler NR. Negative symptoms in schizophrenia: a review and clinical guide for recognition, assessment, and treatment. Neuropsychiatric Dis Treat. 2020;16:519–34. https://doi.org/10.2147/NDT.S225643.
Ahmad N, Strand R, Sparresäter B, Tarai S, Lundström E, Bergström G, Ahlström H, Kullberg J. Automatic segmentation of large-scale ct image datasets for detailed body composition analysis. BMC Bioinfo. 2023;24(1):346.
Ahmad N, Asghar S, Gillani SA. Transfer learning-assisted multi-resolution breast cancer histopathological images classification. Vis Comput. 2022;38(8):2751–70.
Ahmad N, Dahlberg H, Jönsson H, Tarai S, Guggilla RK, Strand R, Lundström E, Bergström G, Ahlström H, Kullberg J. Voxel-wise body composition analysis using image registration of a three-slice ct imaging protocol: methodology and proof-of-concept studies. Biomed Eng. 2024;23(1):42.
Ahmad N, Öfverstedt J, Tarai S, Bergström G, Ahlström H, Kullberg J. Interpretable uncertainty-aware deep regression with cohort saliency analysis for three-slice ct imaging studies, in: Medical Imaging with Deep Learning, 2024.
Hayat M, Ahmad N, Nasir A, Tariq Z. A. Hybrid deep learning efficientnetv2 and vision transformer (effnetv2-vit) model for breast cancer histopathological image classification, IEEE Access (2024).
Noor MBT, Zenia NZ, Kaiser MS, Mamun SA, Mahmud M. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of alzheimer’s disease, parkinson’s disease and schizophrenia. Brain Info. 2020;7:1–21.
Yu T, Pei W, Xu C, Zhang X, Deng C. Prediction of violence in male schizophrenia using smri, based on machine learning algorithms. BMC Psychiatry. 2022;22(1):1–7.
Tyagi A, Singh V. P, Gore M. M. Machine learning approaches for the detection of schizophrenia using structural mri, in: International Conference on Advanced Network Technologies and Intelligent Computing, Springer, 2022, pp. 423–439.
Tyagi A, Singh VP, Gore MM. An efficient automated detection of schizophrenia using k-nn and bag of words features. SN Comput Sci. 2023;4(5):518.
Greenstein D, Weisinger B, Malley J, Clasen L, Gogtay N. Using multivariate machine learning methods and structural mri to classify childhood onset schizophrenia and healthy controls. Front Psychiatry. 2012. https://doi.org/10.3389/fpsyt.2012.00053.
Tyagi A, Singh VP, Gore MM. Detection of schizophrenia from eeg signals using selected statistical moments of mfc coefficients and ensemble learning. Neuroinformatics. 2024;22(4):499–520.
Libero LE, DeRamus TP, Lahti AC, Deshpande G, Kana RK. Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates. Cortex. 2015;66:46–59.
Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev. 2012;36(4):1140–52.
Tyagi A, Singh VP, Gore MM. Towards artificial intelligence in mental health: a comprehensive survey on the detection of schizophrenia. Multimed Tools Appl. 2023;82(13):20343–405.
Akbari H, Sadiq MT, Rehman AU. Classification of normal and depressed eeg signals based on centered correntropy of rhythms in empirical wavelet transform domain. Health Info Sci Syst. 2021;9:1–15.
Zhang T, Davatzikos C. Optimally-discriminative voxel-based morphometry significantly increases the ability to detect group differences in schizophrenia, mild cognitive impairment, and alzheimer’s disease. Neuroimage. 2013;79:94–110.
Pina-Camacho L, Garcia-Prieto J, Parellada M, Castro-Fornieles J, Gonzalez-Pinto AM, Bombin I, Graell M, Paya B, Rapado-Castro M, Janssen J, et al. Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model. European Child Adolesc Psychiatry. 2015;24:427–40.
Zanetti MV, Schaufelberger MS, Doshi J, Ou Y, Ferreira LK, Menezes PR, Scazufca M, Davatzikos C, Busatto GF. Neuroanatomical pattern classification in a population-based sample of first-episode schizophrenia. Prog Neuro-Psychopharmacol Biol Psychiatry. 2013;43:116–25.
Takayanagi Y, Takahashi T, Orikabe L, Mozue Y, Kawasaki Y, Nakamura K, Sato Y, Itokawa M, Yamasue H, Kasai K, et al. Classification of first-episode schizophrenia patients and healthy subjects by automated mri measures of regional brain volume and cortical thickness. PloS One. 2011;6(6): e21047.
Lemm S, Blankertz B, Dickhaus T, Müller K-R. Introduction to machine learning for brain imaging. Neuroimage. 2011;56(2):387–99.
Janousova E, Schwarz D, Kasparek T. Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition. Psychiatry Res: Neuroimaging. 2015;232(3):237–49.
Tyagi A, Singh V. P, Gore M. M. Improved detection of coronary artery disease using dt-rfe based feature selection and ensemble learning, in: International Conference on Advanced Network Technologies and Intelligent Computing, Springer, 2021, pp. 425–440.
Haque UM, Kabir E, Khanam R. Early detection of paediatric and adolescent obsessive-compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms. Health Info Sci Syst. 2023;11(1):31.
Pudjihartono N, Fadason T, Kempa-Liehr AW, O’Sullivan JM. A review of feature selection methods for machine learning-based disease risk prediction. Front Bioinfo. 2022;2: 927312.
Bhagat P, Choudhary P, Singh K. M. A comparative study for brain tumor detection in mri images using texture features, in: Sensors for health monitoring, Elsevier, 2019, pp. 259–287.
Kaplan K, Kaya Y, Kuncan M, Ertunç HM. Brain tumor classification using modified local binary patterns (lbp) feature extraction methods. Med Hypo. 2020;139: 109696.
Kaplan E, Baygin M, Barua PD, Dogan S, Tuncer T, Altunisik E, Palmer EE, Acharya UR. Exhif: Alzheimer’s disease detection using exemplar histogram-based features with ct and mr images. Med Eng Phys. 2023;115: 103971.
Taşcı B, Tasci G, Ayyıldız H, Kamath A, Barua PD, Tuncer T, Dogan S, Ciaccio E, Chakraborty S, Acharya UR. Automated schizophrenia detection model using blood sample scattergram images and local binary pattern. Multimed Tools Appl. 2023. https://doi.org/10.1007/s11042-023-16676-0.
Kumar TS, Rajesh KN, Maheswari S, Kanhangad V, Acharya UR. Automated schizophrenia detection using local descriptors with eeg signals. Eng Appl Artif Intel. 2023;117: 105602.
Rajesh KN, Kumar TS. Schizophrenia detection in adolescents from eeg signals using symmetrically weighted local binary patterns, in. 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE. 2021;2021:963–6.
Aydemir E, Dogan S, Baygin M, Ooi C. P, Barua P. D, Tuncer T, Acharya U. R. Cgp17pat: automated schizophrenia detection based on a cyclic group of prime order patterns using eeg signals, in: Healthcare, Vol. 10, MDPI, 2022, p. 643.
Rani PE, Pavan B. Multi-class eeg signal classification with statistical binary pattern synergic network for schizophrenia severity diagnosis. AIMS Biophys. 2023;10(3):347–71.
Tasci G, Gun MV, Keles T, Tasci B, Barua PD, Tasci I, Dogan S, Baygin M, Palmer EE, Tuncer T, et al. Qlbp: dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using eeg signals. Chaos Solit Fractals. 2023;172: 113472.
Manic KS, Rajinikanth V, Al-Bimani AS, Taniar D, Kadry S. Framework to detect schizophrenia in brain mri slices with mayfly algorithm-selected deep and handcrafted features. Sensors. 2022;23(1):280.
Elakkiya M. K. et al., Toward improving the accuracy in the diagnosis of schizophrenia using functional magnetic resonance imaging (fmri), in: Cognitive Systems and Signal Processing in Image Processing, Elsevier, 2022, pp. 293–318.
Pouyan AA, Shahamat H. A texture-based method for classification of schizophrenia using fmri data. Biocybern Biomed Eng. 2015;35(1):45–53.
Zhang Y-D, Chen S, Wang S-H, Yang J-F, Phillips P. Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int J Imaging Syst Technol. 2015;25(4):317–27.
Saeedi M, Saeedi A, Mohammadi P. Schizophrenia diagnosis via fft and wavelet convolutional neural networks utilizing eeg signals, Pre-Print (2022).
Sun J, Cao R, Zhou M, Hussain W, Wang B, Xue J, Xiang J. A hybrid deep neural network for classification of schizophrenia using eeg data. Sci Rep. 2021;11(1):4706.
Agarwal M, Singhal A. Fusion of pattern-based and statistical features for schizophrenia detection from eeg signals. Med Eng Phys. 2023;112: 103949.
Aslan Z, Akin M. Automatic detection of schizophrenia by applying deep learning over spectrogram images of eeg signals., Traitement du Signal 37 (2) (2020).
Liu L, Cui L-B, Wu X-S, Fei N-B, Xu Z-L, Wu D, Xi Y-B, Huang P, von Deneen KM, Qi S, Zhang Y-H, Wang H-N, Yin H, Qin W. Cortical abnormalities and identification for first-episode schizophrenia via high-resolution magnetic resonance imaging. Biomarkers Neuropsychiatry. 2020;3: 100022. https://doi.org/10.1016/j.bionps.2020.100022.
Latha M, Kavitha G. Combined metaheuristic algorithm and radiomics strategy for the analysis of neuroanatomical structures in schizophrenia and schizoaffective disorders. IRBM. 2021;42(5):353–68. https://doi.org/10.1016/j.irbm.2020.10.006.
Wu Y, Ren P, Chen R, Xu H, Xu J, Zeng L, Wu D, Jiang W, Tang N, Liu X. Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression, Brain Imaging Behav (2022) 1–10.
Chen Z, Yan T, Wang E, Jiang H, Tang Y, Yu X, Zhang J, Liu C, et al., Detecting abnormal brain regions in schizophrenia using structural mri via machine learning, Comput Intel Neurosci 2020 (2020).
Sendi MS, Zendehrouh E, Turner JA, Calhoun VD. Dynamic patterns within the default mode network in schizophrenia subgroups, in. 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE. 2021;2021:1640–3.
Rokham H, Pearlson G, Abrol A, Falakshahi H, Plis S, Calhoun VD. Addressing inaccurate nosology in mental health: a multilabel data cleansing approach for detecting label noise from structural magnetic resonance imaging data in mood and psychosis disorders. Biol Psychiatry: Cognitive Neurosci Neuroimaging. 2020;5(8):819–32. https://doi.org/10.1016/j.bpsc.2020.05.008.
Qureshi MNI, Oh J, Cho D, Jo HJ, Lee B. Multimodal discrimination of schizophrenia using hybrid weighted feature concatenation of brain functional connectivity and anatomical features with an extreme learning machine. Front Neuroinfo. 2017. https://doi.org/10.3389/fninf.2017.00059.
Comments (0)