Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder

Mahmud, Mufti; Kaiser, M. Shamim; Rahman, Muhammad Arifur; Wadhera, Tanu; Brown, David J.; Shopland, Nicholas; Burton, Audrey; Hughes-Roberts, Thomas; Mamun, Shamim Al; Ieracitano, Cosimo; Tania, Marzia Hoque; Moni, Mohammad Ali; Islam, Sheikh Mohammed Shariful; Ray, Kanad; Hossain, Mohammad Shahadat

doi:10.1007/978-3-031-05039-8_26

Cited by 57 publications

(10 citation statements)

References 27 publications

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“…As these women become older, they learn and adapt methods to better manage the autism they are diagnosed with. With the progress of technology and artificial intelligence, ASD may be detectable at a younger age [59]. Early detection of ASD-like symptoms through technology and identification of specific comorbidity traits would be beneficial so that these individuals can learn how to manage ASD [60,61].…”

Section: Discussionmentioning

confidence: 99%

Quality of Life, Neurosensory Disorders and Co-Occurring Medical Conditions in Individuals on the Spectrum, with a Special Focus on Females Diagnosed with Autism: A Systematic Review

Davies

Moosa

McKenna

et al. 2023

JCM

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that has a high prevalence and a significant economic impact. Our knowledge regarding neurosensory disorders and co-occurring medical conditions in the ASD population is limited, particularly for autistic women. Most of the studies include male participants or do not make comparisons with their female counterparts. The objective of this systematic review article is to explore the quality of life as well as the prevalence of neurosensory disorders and co-occurring medical conditions in individuals on the spectrum, with a special focus on autistic females. The literature search was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. A protocol of this systematic review was designed a priori and was registered in the PROSPERO database (registration number: CRD42022330368). We concluded that numerous medical areas were of concern. Autistic females are more likely than their male counterparts with ASD to suffer from psychiatric conditions such as post-traumatic stress syndrome, depression, and eating disorders. They are also more likely to report GI-related disturbances and chronic pain. Further investigations are warranted to determine quality of life, as well as the prevalence and severity of neurosensory disorders in individuals with ASD, specifically studies comparing autistic females with their male counterparts. The information derived from these studies will help develop better support systems for individuals with autism, particularly females on the spectrum, in pursuit of improving their quality of life.

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Section: Discussionmentioning

confidence: 99%

Quality of Life, Neurosensory Disorders and Co-Occurring Medical Conditions in Individuals on the Spectrum, with a Special Focus on Females Diagnosed with Autism: A Systematic Review

Davies

Moosa

McKenna

et al. 2023

JCM

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“…In recent years artificial intelligence (AI) has been applied in diverse problem domains to solve various challenging problems, including text classification [13,14,15,16], cyber security [17,18,19,20], neurological disease detection [21,22,23,12] and management [24,25,26,27,28,29], elderly care [30,31], fighting pandemic [32,33,34,35,36,37,38], and healthcare service delivery [39,40,41]. In particular, deep learning (DL) has attracted a lot of attention [6,7].…”

Section: Related Workmentioning

confidence: 99%

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (CNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pre-trained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer's Disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.

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“…The author of [62] proposed an algorithm that trains a model from multiple distributed sources in order to ensure privacy in medical image segmentation. Sarkar et al used FL for Pneumonia diagnosis [63], and Mahmud et al pointed out its possible usage in autism research [28].…”

Section: Introductionmentioning

confidence: 99%

Detection of Osteosarcoma using Deep Feature Extraction with Federated Learning and MLP from Histopathological Images

Chowdhury,

Mahmud,

Elahe

et al. 2023

Preprint

Self Cite

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Background: The most common type of cancerous bone tumour is Osteosarcoma. Microscopic analysis of coloured slides is mostly used for Osteosarcoma diagnosis. As Osteosarcoma is highly heterogeneous, pathologists struggle to get a consensus diagnosis. Moreover, manual analysis of microscopic images is laborious, time-consuming, and vulnerable to bias. Method: In this research, a framework that extracts features based on CNN is developed to predict Osteosarcoma to address previous studies' performance gaps and shortcomings. The dataset used for this experiment is obtained from pathology archives at Children's Medical Center in Dallas, which includes 1144 histology glass slides. First, data augmentation is used to extend the dataset size. This study found Blur, Contrast, and Random augmentation perform well for the Osteosarcoma dataset. Next, we primarily considered six pre-trained transfer learning (TL) models and modified the models to extract features. Then, we examined feature selectors like Genetic Algorithm, Principal Component Analysis, Recursive Feature Elimination and Baruto and found PCA selects the optimal feature set. Finally, we fed the selected features into a fine-tuned MLP called Grid search MLP (GsMLP) and utilised federated learning (FL) to ensure the diversity and privacy of the dataset. Results: Experiments have revealed that the modified Xception model performs better in extracting features for Osteosarcoma prediction. Also, the proposed framework performed better than state-of-the-art techniques such as Convolutional Neural Network, VGG19, Random Forest, and AdaBoost with 98.20% accuracy and 97.5% Receiver Operating Characteristic - Area Under the Curve.

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Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder

Cited by 57 publications

References 27 publications

Quality of Life, Neurosensory Disorders and Co-Occurring Medical Conditions in Individuals on the Spectrum, with a Special Focus on Females Diagnosed with Autism: A Systematic Review

Quality of Life, Neurosensory Disorders and Co-Occurring Medical Conditions in Individuals on the Spectrum, with a Special Focus on Females Diagnosed with Autism: A Systematic Review

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Detection of Osteosarcoma using Deep Feature Extraction with Federated Learning and MLP from Histopathological Images

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