Abstract
Medical imaging systems provide researchers and healthcare workers information which is helpful in imparting improved diagnosis, better prediction, and treatment for the illness. Medical data collection is very important as it eventually helps in providing a patient good care and treatment. In recent years, machine learning and deep learning approaches have played a significant role in the analysis of neuroimaging data. In this paper, we have addressed one of the disorders associated with the human brain, known as schizophrenia. It is a psychotic disorder which makes the person interpret things around the environment abnormally. In the next section, we have enlightened schizophrenia and medical imaging relations, various types of neuroimaging techniques that include CT scan, MRI (magnetic resonance imaging), fMRI (functional MRI), PET (positron emission tomography), and comparison among them. Also, we have discussed different machine learning and deep learning frameworks and techniques used in this area. Finally, we have concluded our chapter with the future scope, upcoming challenges, and conclusions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Algumaei AH et al (2022) Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data. PLoS One 17(5):e0265300
Azhari EEM, Hatta MMM, Htike ZZ, Win SL (2014) Tumor detection in medical imaging: a survey. Int J Adv Inf Technol 4(1):21
Bashyam VM et al (2020) Medical image harmonization using deep learning based canonical mapping: toward robust and generalizable learning in imaging. arXiv:2010.05355
Chand GB et al (2020) Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain 143(3):1027–1038
Chatterjee I et al (2019) Identification of brain regions associated with working memory deficit in schizophrenia. F1000Res 8:124
Chatterjee I et al (2020a) Identification of changes in grey matter volume using an evolutionary approach: an MRI study of schizophrenia. Multimedia Syst 26(4):383–396
Chatterjee I et al (2020b) Impact of ageing on the brain regions of the schizophrenia patients: an fMRI study using evolutionary approach. Multimed Tools Appl 79(33):24757–24779
Ganguly D et al (2010) Medical imaging: a review. In: International conference on security-enriched urban computing and smart grid. Springer, Berlin, Heidelberg
Gautam A, Chatterjee I (2021) An overview of big data applications in healthcare: opportunities and challenges. In: Knowledge modelling and big data analytics in healthcare. CRC, Boca Raton, pp 21–36
Glaser JI et al (2019) The roles of supervised machine learning in systems neuroscience. Prog Neurobiol 175:126–137
Hainc N et al (2017) The bright, artificial intelligence-augmented future of neuroimaging reading. Front Neurol 8:489
Jauhar S et al (2018) Is there a symptomatic distinction between the affective psychoses and schizophrenia? A machine learning approach. Schizophr Res 202:241–247
Jilka S et al (2022) Identifying schizophrenia stigma on twitter: a proof of principle model using service user supervised machine learning. NPJ Schizophr 8(1):1–8
Kim M, Yun J, Cho Y, Shin K, Jang R, Bae HJ, Kim N (2019) Deep learning in medical imaging. Neurospine 16(4):657
Kunio D (2006) Diagnostic imaging over the last 50 years: research and development in medical imaging science and technology. Phys Med Biol 51(13):R5
Lin E, Lin C-H, Lane H-Y (2021) Applying a bagging ensemble machine learning approach to predict the functional outcome of schizophrenia with clinical symptoms and cognitive functions. Sci Rep 11:1–9
Nygård M et al (2019) Patients with schizophrenia have impaired muscle force-generating capacity and functional performance. Scand J Med Sci Sports 29(12):1968–1979
Sadeghi D et al (2021) An overview on artificial intelligence techniques for diagnosis of schizophrenia based on magnetic resonance imaging modalities: methods, challenges, and future works. arXiv: 2103.03081
Sadeghi D et al (2022) An overview of artificial intelligence techniques for diagnosis of schizophrenia based on magnetic resonance imaging modalities: methods, challenges, and future works. Comput Biol Med 146:105554
Salloum SA et al (2020) Machine learning and deep learning techniques for cybersecurity: a review. In: The International conference on artificial intelligence and computer vision. Springer, Cham
Schnack, Hugo G (2019) Improving individual predictions: machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases). Schizophr Res 214:34–42
Serte S, Serener A, Al-Turjman F (2020) Deep learning in medical imaging: a brief review. Trans Emerg Telecommun Technol:e4080
Shalbaf A, Bagherzadeh S, Maghsoudi A (2020) Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med 43(4):1229–1239
Shinde PP, Shah S (2018) A review of machine learning and deep learning applications. In: 2018 Fourth international conference on computing communication control and automation (ICCUBEA). IEEE, New York
Song P et al (2020) Investigation of sex hormones on the early diagnosis of schizophrenia. In: Medical imaging 2020: computer-aided diagnosis, vol 11314. International Society for Optics and Photonics, Bellingham
Storrs KR, Kriegeskorte N (2019) Deep learning for cognitive neuroscience. arXiv:1903.01458
Vogt N (2018) Machine learning in neuroscience. Nat Methods 15(1):33–33
Wintermark M et al (2018) The vast potential and bright future of neuroimaging. Br J Radiol 91(1087):20170505
Yu W et al (2018) Magnetic resonance imaging study of gray matter in schizophrenia based on XGBoost. J Integr Neurosci 17(4):331–336
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Gautam, A., Chatterjee, I. (2023). Medical Imaging and Schizophrenia: A Study on State-of-Art Applications. In: Chatterjee, I. (eds) Cognizance of Schizophrenia:: A Profound Insight into the Psyche. Springer, Singapore. https://doi.org/10.1007/978-981-19-7022-1_16
Download citation
DOI: https://doi.org/10.1007/978-981-19-7022-1_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7021-4
Online ISBN: 978-981-19-7022-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)