A Convolutional Neural Network Combined With Prototype Learning Framework for Brain Functional Network Classification of Autism Spectrum Disorder

Liang, Yin; Liu, Baolin; Zhang, Hesheng

doi:10.1109/tnsre.2021.3120024

Cited by 26 publications

(10 citation statements)

References 50 publications

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“…The deep learning approach has recently achieved promising results in analysing medical images [64]. A CNN can automatically represent features from training data and update parameters for convolutional filters during training [65]. The convolutional neural network architecture consists of several layers, such as an input layer, hidden layers, and an output layer [66].…”

Section: Convolution Neural Network Overviewmentioning

confidence: 99%

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

Farhan,

Khalid,

Manzoor

2023

Preprint

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Brain tumors are the most prevalent and life-threatening cancer; an early and accurate diagnosis of brain tumors increases the chances of patient survival and treatment planning. However, manual tumor detection is a complex, cumbersome and time-consuming task and is prone to errors, which relies on the radiologist’s experience. As a result, the development of accurate and automatic system for tumor detection is critical. In this paper, we proposed a new model called Parallel Residual Convolutional Network (PRCnet) model to classify brain tumors from Magnetic Resonance Imaging. The PCRnet model uses several techniques (such as filters of different sizes with parallel layers, connections between layers, batch normalization layer, and ReLU) and dropout layer to overcome the over-fitting problem, for achieving accurate and automatic classification of brain tumors. The PRCnet model is trained and tested on two different datasets and obtained an accuracy of 94.77% and 97.1% for dataset A and dataset B, respectively which is way better as compared to the state-of-the-art models.

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Section: Convolution Neural Network Overviewmentioning

confidence: 99%

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

Farhan,

Khalid,

Manzoor

2023

Preprint

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“…The widely utilized atlas is the automatic anatomical labeling (AAL) atlas (Tzourio-Mazoyer et al, 2002 ; Guo and Zhang, 2020 ; Ji and Yao, 2021 ; Pang et al, 2021 ; Wang et al, 2021 , 2022b ; Alorf and Khan, 2022 ; Cai et al, 2022 ; Hu et al, 2022 ; Lu et al, 2022 ; Wang T. et al, 2022 ; Chen et al, 2023 ). Besides, FC can be constructed by other atlases, such as the Power atlas (Power et al, 2011 ; Xu et al, 2020 ), Craddock 200 atlas (Craddock et al, 2011 ; Huang et al, 2021 ; Liang et al, 2021 , 2022 ), Bootstrap Analysis of Stable Clusters (Bellec et al, 2010 ; Subah et al, 2021 ; Wang N. et al, 2022 ), Brainnetome atlas (Fan et al, 2016 ; Jin et al, 2020 ), Yeo atlas (Yeo et al, 2011 ; Gullett et al, 2021 ), Harvard-Oxford atlas (Desikan et al, 2006 ; Cao et al, 2020 ), and Dosenbach atlas (Dosenbach et al, 2010 ; Zhao et al, 2022 ). In particular, Zhang et al ( 2022 ) constructed multiple FCNs based on the selected set of the atlas from generated multiple personalized atlases from the AAL atlas to improve the diagnosis effect of MCI.…”

Section: Features Extracted From Fmri Datamentioning

confidence: 99%

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

Teng,

Mi,

Shi

et al. 2023

Front. Neurosci.

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Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.

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“…Ref. [ 59 ] generated virtual brain networks using fMRI data and developed a unique CNN to diagnose ASD. Ref.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

et al. 2022

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Autism spectrum is a brain development condition that impairs an individual’s capacity to communicate socially and manifests through strict routines and obsessive–compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81–84%, with a normalized discounted cumulative gain of 79–81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models’ treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.

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A Convolutional Neural Network Combined With Prototype Learning Framework for Brain Functional Network Classification of Autism Spectrum Disorder

Cited by 26 publications

References 50 publications

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

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