Related references
Note: Only part of the references are listed.
Article
Neurosciences
K. K. Mujeeb Rahman et al.
Summary: This study investigates the applicability of static features extracted from autistic children's face photographs as a biomarker to distinguish them from typically developing children. By using five pre-trained CNN models and a DNN model, we are able to accurately identify autism in children.
Article
Medicine, General & Internal
Mujeeb Rahman Kanhirakadavath et al.
Summary: Autism spectrum disorder is characterized by difficulties in social skills, repetitive activities, speech, and nonverbal communication. This study investigated the use of machine learning methods on eye-tracking data to aid in early screening of autism. The findings suggest that a deep neural network model outperformed traditional machine learning approaches, providing a quick and reliable tool for clinicians to screen for autism.
Article
Mathematical & Computational Biology
Fawaz Waselallah Alsaade et al.
Summary: Autism spectrum disorder (ASD) is a mental illness that can be detected using social media data and biomedical images. The proposed research aims to design a system for ASD detection based on social media and face recognition, using deep learning techniques and a simple web application. The Xception model achieved the highest accuracy result in classifying ASD.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Article
Psychiatry
Junaid Asghar et al.
Summary: When assessing the efficacy of antidepressant treatment for MDD, it is important to consider the diversity of symptoms. In addition to the conventional HAM-D scale, a unidimensional analysis of key factors may provide more useful information.
FRONTIERS IN PSYCHIATRY
(2022)
Article
Medicine, General & Internal
Theyazn H. H. Aldhyani et al.
Summary: Skin, as the primary protective layer of internal organs, is increasingly prone to various diseases due to pollution and other factors. To address this issue, a lightweight and efficient model for accurate classification of skin lesions is proposed, utilizing dynamic-sized kernels and ReLU/leakyReLU activation functions. The model achieves high overall accuracy and outperforms state-of-the-art heavy models.
Article
Medicine, General & Internal
Theyazn H. H. Aldhyani et al.
Summary: Breast cancer is a disease caused by abnormal replication of breast cells and is a global issue that results in daily deaths among women. Early detection of breast cancer is crucial for saving lives, and deep learning and machine learning models play a significant role in its detection.
Proceedings Paper
Engineering, Biomedical
Huong Pham et al.
Summary: This study investigates the feasibility of using a new quantitative imaging marker to predict survival of gastric cancer patients by applying computer-aided detection schemes. The results show that radiomics features computed from CT images can effectively predict the survival of gastric cancer patients, and the fusion of quasi-3D image features yields higher prediction accuracy than using simple 2D image features.
MEDICAL IMAGING 2022: COMPUTER-AIDED DIAGNOSIS
(2022)
Article
Engineering, Multidisciplinary
Rezaul Haque et al.
Summary: Social networks play a crucial role in detecting suicidal ideation, with machine learning and deep learning models showing promising results in identifying early indications accurately. By analyzing social media data, valuable insights can be gained to prevent suicide attempts and potentially save lives.
Article
Health Care Sciences & Services
Md Manjurul Ahsan et al.
Summary: This paper explores the application of machine learning in the early diagnosis of diseases and conducts a bibliometric analysis to identify the most influential authors, countries, organizations, and articles. It summarizes the recent trends and approaches in machine-learning-based disease diagnosis, considering factors such as algorithms, disease types, data types, applications, and evaluation metrics. The paper highlights key findings and provides insights into future trends and opportunities in the MLBDD area.
Article
Automation & Control Systems
Puneet Mishra et al.
Summary: This study introduces the concept of transfer learning (TL) to update deep learning models related to spectral data, demonstrating successful model updates to new scenarios and outperforming traditional model updating methods.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2021)
Article
Neurosciences
Ming Yang et al.
Summary: Brain functional networks (BFNs) constructed from resting-state fMRI data were used to classify patients with autism spectrum disorder (ASD), showing improved detection accuracy and the potential of large-scale BFNs as reliable biomarkers for ASD diagnosis.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Multidisciplinary Sciences
Nada Kojovic et al.
Summary: Recent clinical research in autism focuses on single feature extraction, utilizing deep neural networks to differentiate between ASD children and typically developing peers. The model achieved an 80.9% accuracy with prediction probability positively correlated to the overall level of symptoms of autism. The study suggests the potential of reliable machine-learning-based ASD screening in the near future.
SCIENTIFIC REPORTS
(2021)
Article
Neurosciences
Tania Akter et al.
Summary: Early detection of ASD is crucial for devising appropriate therapeutic plans, and this study presents an improved MobileNet-V1 model for more precise identification of children with ASD using facial images and machine learning techniques. The model shows high accuracy and low error rates, performing well in identifying different sub-types of ASD.
Article
Health Care Sciences & Services
Md Manjurul Ahsan et al.
Summary: The COVID-19 global pandemic posed significant challenges in healthcare, with patient isolation driven by PCR testing initially challenged by lower availability and higher costs in developing countries. Researchers proposed COVID-19 patient screening using Chest CT and X-ray results, alongside AI and deep learning for higher diagnostic accuracy. Various models were tested, with MobileNetV2 showing the best performance and VGG16 excelling in X-ray datasets, supported by previous academic literature highlighting the effectiveness of these methods in COVID-19 diagnoses.
Article
Engineering, Multidisciplinary
Md Manjurul Ahsan et al.
Summary: The study aimed to evaluate the performance of machine learning algorithms on a dataset of patients with heart disease, with the results showing that CART algorithm combined with RS or QT data scaling method outperformed all others.
Article
Medical Informatics
Md Delowar Hossain et al.
Summary: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder often characterized by sensory issues. Machine learning based intelligent diagnosis has evolved to complement traditional clinical methods for improved diagnosis. Experimental results show that multilayer perceptron (MLP) classifier outperforms other benchmark techniques for ASD diagnosis.
HEALTH INFORMATION SCIENCE AND SYSTEMS
(2021)
Article
Computer Science, Information Systems
Md Manjurul Ahsan et al.
Summary: The study proposes and tests six modified deep learning models to detect SARS-CoV-2 infection from chest X-ray images, with VGG16 and MobileNetV2 showing promising results in accurately identifying patients with COVID-19 symptoms on both datasets. The poorly performing models in Study One significantly improved their accuracy when trained with the larger dataset in Study Two, while models like InceptionResNetV2 and VGG19 demonstrated high accuracy on both datasets, providing an accessible alternative to identify patients with COVID-19.
Article
Psychology, Developmental
Katharine E. Zuckerman et al.
Summary: The American Academy of Pediatrics recommends autism spectrum disorder screening at 18- and 24-month well-child visits, but many low-income and minority children are not diagnosed until school age. Offering screening at preschool well-child visits may help reduce disparities and improve early diagnosis of autism spectrum disorder.
Article
Multidisciplinary Sciences
Md Manjurul Ahsan et al.
Article
Computer Science, Artificial Intelligence
Md Manjurul Ahsan et al.
MACHINE LEARNING AND KNOWLEDGE EXTRACTION
(2020)
Article
Medical Informatics
Fadi Thabtah et al.
HEALTH INFORMATION SCIENCE AND SYSTEMS
(2019)
Article
Computer Science, Information Systems
Tania Akter et al.
Proceedings Paper
Engineering, Electrical & Electronic
Md. Shahriare Satu et al.
2019 1ST INTERNATIONAL CONFERENCE ON ROBOTICS, ELECTRICAL AND SIGNAL PROCESSING TECHNIQUES (ICREST)
(2019)
Article
Computer Science, Information Systems
Gozde Yolcu et al.
MULTIMEDIA TOOLS AND APPLICATIONS
(2019)
Article
Computer Science, Information Systems
Fadi Thabtah et al.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2018)
Review
Medicine, General & Internal
Catherine Lord et al.
Article
Neurosciences
Stefanie Schelinski et al.
Social Cognitive and Affective Neuroscience
(2016)
Review
Behavioral Sciences
Quentin Guillon et al.
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
(2014)
Article
Psychology, Developmental
David S. Mandell
JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS
(2008)