January 2024
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Publications (4)
January 2024
International Journal of Medical Engineering and Informatics
October 2023
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12 Reads
Lecture Notes in Electrical Engineering
MRI is the most influential and flexible tool used for the diagnosis of various pathological alterations of living tissues. Identification of structural abnormalities in brain has become a rising attention for the early prediction and diagnosis of schizophrenia from the healthy controls. Recognizing the biomarkers in schizophrenic patients and related conditions will be highly beneficial in discriminating the psychic disorder. Neuro imaging explains the brain images by using the information provided by either of these techniques like computed tomography, magnetic resonance imaging and positron emission tomography, out of which Structural neuroimaging studies can provide some of the most consistent evidence for brain abnormalities in schizophrenia. The motivation of the work is to get more significant diagnostic data. As the electronically captured images might be inappropriate and needed to be fine-tuned such as removing the noise, separating different shades using segmentation operations and many more. To analyze the magnetic resonance imaging methodologies used in non-invasive clinical investigations more affectively, these can be upgraded by the application of suitable optimization methods. In many studies it is evident that the conventional mathematical models are not enough in obtaining better solutions for real-time applications. Coming to medical imaging accuracy is playing much more important role in early diagnosis and treatment suggestions. To obtain the best solution medical imaging and diagnosis optimization techniques are playing a much more important role nowadays. Especially nature-inspired algorithms. Here a detailed survey has been made to show how the optimization techniques can be used effectively in obtaining accurate data to characterize disease-related alterations in brain structures and used for early diagnosis.
January 2022
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42 Reads
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2 Citations
Background: The importance of identifying the structural and functional abnormalities in the brain in early prediction and diagnosis of schizophrenia has grabbed the attention of both the neuroimaging scientist and clinician. Objective: The purpose of this study is to structure a review paper that find out the condition and recognize specific biomarkers of the schizophrenic brain. Method: Neuroimaging can be used to characterize brain structure, function, and chemistry by different non-invasive techniques such as computed tomography, magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography. The abnormalities in the brain can be used to discriminate the psychic disorder like schizophrenia from others. To find disease-related brain alterations in neuroimaging, structural neuroimaging studies provide the most consistent evidence in most of the studies. The review discusses the major issues and findings in structural neuroimaging studies of schizophrenia. In particular, the data is collected from different papers where they concentrated on the brain affected regions of different subjects and made a conclusion out of it. Result: In this work, a detailed survey has been made for finding the structural abnormalities in the brain from different neuroimaging techniques. Several image processing methods are used to acquire the brain images. Different Machine learning techniques, Optimization methods and Pattern recognition methods are used to predict the disease with specific biomarkers, and their results are emphasized. Thus, in this work, it is also highlighted the deep learning which shows a promising role in obtaining neuroimaging data to characterize disease-related alterations in brain structure.
Citations (1)
... SCZ was reported to be related to neuronallevel changes causing cortical excitation-inhibition imbalance [12] [13]. However, it can be difficult to conduct an accurate SCZ/BPD diagnosis only from the neuro-imaging perspective [14][15] [16] [17]. ...
- Citing Article
January 2022