Functional Connectivity Based Classification of ADHD Using Different Atlases

Salman, Sartaj Ahmed; Lian, Zhichao; Saleem, Marva; Zhang, Yuduo

doi:10.1109/pic50277.2020.9350749

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…In Lahore, Pakistan, our study incorporated several additional hierarchical ELM elements, and the results showed that these features were effective in differentiating normal people from those who had ADHD. The conclusive findings have been categorized as being precisely the same as Ahmad's earlier investigations [28]. In addition to this, it demonstrates a high level of accuracy by making use of the most recent technological advancements.…”

Section: Discussionsupporting

confidence: 67%

“…In 2020, Salman et al [29] have used FC of the brain based on fMRI. Initially, raw data on ADHD was collected and the noise was removed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The challenges experienced during the detection of ADHD are given as follows,  The new technology helped to detect ADHD accurately [29][30][34] [35]. However, the tested samples are too small.…”

Section: Challengesmentioning

confidence: 99%

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Classification of ADHD with the Functional Connectivity by Usage of Different Atlases in Lahore, Pakistan

Fahad Saddique

2023

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Attention Deficit-Hyperactivity Disorder (ADHD) is a psychiatric condition that affects children's abilities. Nowadays computational diagnosis strategies of neuropsychiatric disorders are gaining more attention. Diagnosing this disorder based on fMRI is critical to determine the brain's Functional Connectivity (FC). Millions of children have the symptoms of this disease.The brain is notoriously unreliable for diagnosing neurological conditions. This condition is referred to as a chronic disease.A great number of youngsters exhibit signs of this disease. As a result, the study endeavored to come up with a model and design that is both reliable and accurate for diagnosing ADHD.A variety of techniques used in this present study, such as the local binary encoding method (LBEM) is utilized for future extraction, and the hierarchical extreme learning machine (HELM)is used to extract information on the connectivity functionalities of the brain.To validate our approach, the data of One hundred fifty-three children serve as a sample for the diagnosis, from which one hundred children are ultimately determined to have ADHD.These affected ADHD children are used for our experimental purpose. According to the findings of the research, the results are based on comparing various Atlases given as AAL, CC200, and CC400. Our model gainssuperior performance with CC400 when comparedwith other Atlases.

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Section: Discussionsupporting

confidence: 67%

“…In 2020, Salman et al [29] have used FC of the brain based on fMRI. Initially, raw data on ADHD was collected and the noise was removed.…”

Section: Literature Reviewmentioning

confidence: 99%

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Classification of ADHD with the Functional Connectivity by Usage of Different Atlases in Lahore, Pakistan

Fahad Saddique

2023

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“…However, this model is inefficient in the modern technological age, where many datasets are being generated daily for medical diagnosis. Recently, explainable deep learning (XDL) has gained significant interest for studying the "black box" nature of DL networks in healthcare [15,19,20]. Using XDL methods, researchers, developers, and end users can develop transparent DL models that explain their decisions clearly.…”

Section: Introductionmentioning

confidence: 99%

Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class Activation Mapping

Hussain,

Shouno

2023

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Brain tumors (BT) present a considerable global health concern because of their high mortality rates across diverse age groups. A delay in diagnosing BT can lead to death. Therefore, a timely and accurate diagnosis through magnetic resonance imaging (MRI) is crucial. A radiologist makes the final decision to identify the tumor through MRI. However, manual assessments are flawed, time-consuming, and rely on experienced radiologists or neurologists to identify and diagnose a BT. Computer-aided classification models often lack performance and explainability for clinical translation, particularly in neuroscience research, resulting in physicians perceiving the model results as inadequate due to the black box model. Explainable deep learning (XDL) can advance neuroscientific research and healthcare tasks. To enhance the explainability of deep learning (DL) and provide diagnostic support, we propose a new classification and localization model, combining existing methods to enhance the explainability of DL and provide diagnostic support. We adopt a pre-trained visual geometry group (pre-trained-VGG-19), scratch-VGG-19, and EfficientNet model that runs a modified form of the class activation mapping (CAM), gradient-weighted class activation mapping (Grad-CAM) and Grad-CAM++ algorithms. These algorithms, introduced into a convolutional neural network (CNN), uncover a crucial part of the classification and can provide an explanatory interface for diagnosing BT. The experimental results demonstrate that the pre-trained-VGG-19 with Grad-CAM provides better classification and visualization results than the scratch-VGG-19, EfficientNet, and cutting-edge DL techniques regarding visual and quantitative evaluations with increased accuracy. The proposed approach may contribute to reducing the diagnostic uncertainty and validating BT classification.

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