Automated detection of schizophrenia using deep learning: a review for the last decade

Sharma, Manish; Patel, Ruchit Kumar; Garg, Aditi; Tan, Ru‐San; Acharya, U. Rajendra

doi:10.1088/1361-6579/acb24d

Cited by 15 publications

(3 citation statements)

References 101 publications

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“…Transfer learning, a strategy leveraging data from related tasks, can partially address this issue, improving the model’s performance. However, it does not entirely replace the requirement for original data ( Sharma et al, 2023 ). Another challenge involves dealing with unbalanced data, which is common in biological datasets where negative samples often outnumber positive ones.…”

Section: Challenges In Classification Of Schizophrenia Using ML and Dlmentioning

confidence: 99%

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

Rahul,

Sharma,

Sharma

et al. 2024

Front. Hum. Neurosci.

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The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI’s potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.

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Section: Challenges In Classification Of Schizophrenia Using ML and Dlmentioning

confidence: 99%

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

Rahul,

Sharma,

Sharma

et al. 2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Traditional machine learning algorithms are hampered by the high cost of annotating todays medical data sets [3]. In 2012, Alex Krizhevskys team proposed the Alex Net model in a competition, which successfully promoted the large-scale use of DL.…”

Section: Research Status 21 Main Application Scenariosmentioning

confidence: 99%

Research progress of artificial intelligence in medical imaging field

Wang

2024

AETR

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With the rapid development of medical equipment, medical imaging has entered the era of big data. How to process massive data quickly and accurately extract critical information for disease diagnosis and treatment is a major challenge facing clinical practice. Artificial intelligence has unique advantages in real-time processing, prediction and analysis, transfer learning, etc., which provides a new breakthrough for solving this problem. This review reviews the artificial intelligence models applied to medical image processing in recent years, shows their wide application scenarios, briefly discusses their unique advantages from three aspects of image recognition, image enhancement and image registration, and finally summarizes the current research status and puts forward some prospects.

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“…Medical Imaging: AlexNet's CNN architecture was related to medical imaging for various diseases. It has been used for the classification of medical images, such as Xrays, MRIs, CT scans, and electroencephalograms, aiding in the diagnosis of diseases, including cancer [47], neurological disorders [48], and schizophrenia [49]. By adapting AlexNet to medical data, researchers and healthcare professionals have improved accuracy and efficiency in disease detection and diagnosis.…”

Section: Applicationsmentioning

confidence: 99%

Review of AlexNet for Medical Image Classification

Tang,

Sun,

Wang

et al. 2023

EAI Endorsed Trans e-Learn

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In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and exploding, etc. AlexNet first utilizes the dropout technique to mitigate overfitting and the ReLU activation function to avoid gradient vanishing. Therefore, we focus our discussion on AlexNet, which has contributed greatly to the development of CNNs in 2012. After reviewing over 40 papers, including journal papers and conference papers, we give a narrative on the technical details, advantages, and application areas of AlexNet.

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Automated detection of schizophrenia using deep learning: a review for the last decade

Cited by 15 publications

References 101 publications

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

Research progress of artificial intelligence in medical imaging field

Review of AlexNet for Medical Image Classification

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