Schizophrenia Detection using Brain MRI — A Study with Watershed Algorithm

Arunmozhi, S.; Raja, N. Sri Madhava; Rajinikanth, V.; Aparna, K; Vallinayagam, V.

doi:10.1109/icscan49426.2020.9262281

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…This work implemented a binary classification to detect the disease with superior accuracy. The recent work of Arunmozhi et al [ 15 ] implemented a joint thresholding and segmentation procedure to extract and evaluate the SCZ from brain MRI slices. Finally, Cetin-Karayumak et al [ 16 ] presented a method to discuss the white matter (WM) abnormality in the brain in SCZ patients.…”

Section: Related Earlier Workmentioning

confidence: 99%

Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features

Manic

Rajinikanth

Al-Bimani

et al. 2022

Sensors

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Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices.

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Section: Related Earlier Workmentioning

confidence: 99%

Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features

Manic

Rajinikanth

Al-Bimani

et al. 2022

Sensors

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show abstract

“…To achieve better detection accuracy, this study implemented a preprocessing image procedure to treat the raw renal CT using a threshold filter approach discussed in earlier research. In the earlier studies, this arrangement is considered to strip the skull region from the brain MRI slices (6,7) and to remove the artifact in lung CT slices (8)(9)(10). A similar procedure is adopted in this study to remove the artifact in RCT slices to improve the visibility of the kidney section.…”

mentioning

confidence: 99%

A framework to distinguish healthy/cancer renal CT images using the fused deep features

Rajinikanth

Vincent

Srinivasan

et al. 2023

Front. Public Health

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IntroductionCancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management.MethodsThe proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation.Results and discussionThis experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant.

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An OTP Framework for effective tumor segmentation and analysis in brain MRI images

Bhima¹,

Neelakantappa²,

Ramaiah³

et al. 2023

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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Schizophrenia Detection using Brain MRI — A Study with Watershed Algorithm

Cited by 3 publications

References 11 publications

Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features

Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features

A framework to distinguish healthy/cancer renal CT images using the fused deep features

An OTP Framework for effective tumor segmentation and analysis in brain MRI images

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