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Artificial intelligence and precision medicine: a new frontier for the treatment of brain tumors
AK Philip, BA Samuel, S Bhatia, SAM Khalifa… - Life, 2022 - mdpi.com
Brain tumors are a widespread and serious neurological phenomenon that can be life-
threatening. The computing field has allowed for the development of artificial intelligence …
threatening. The computing field has allowed for the development of artificial intelligence …
Diagnosing COVID-19 using artificial intelligence: A comprehensive review
Abstract In early March 2020, the World Health Organization (WHO) proclaimed the novel
COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection …
COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection …
High accuracy food image classification via vision transformer with data augmentation and feature augmentation
X Gao, Z **ao, Z Deng - Journal of Food Engineering, 2024 - Elsevier
Food image classification is an important research direction in the field of computer vision
and machine learning. However food image classification faces great challenges when …
and machine learning. However food image classification faces great challenges when …
[HTML][HTML] Improving the robustness and quality of biomedical cnn models through adaptive hyperparameter tuning
Deep learning is an obvious method for the detection of disease, analyzing medical images
and many researchers have looked into it. However, the performance of deep learning …
and many researchers have looked into it. However, the performance of deep learning …
Multilayer perceptron-based prediction of stroke mimics in prehospital triage
Z Zhang, D Zhou, J Zhang, Y Xu, G Lin, B **… - Scientific Reports, 2022 - nature.com
The identification of stroke mimics (SMs) in patients with stroke could lead to delayed
diagnosis and waste of medical resources. Multilayer perceptron (MLP) was proved to be an …
diagnosis and waste of medical resources. Multilayer perceptron (MLP) was proved to be an …
An adaptation of hybrid binary optimization algorithms for medical image feature selection in neural network for classification of breast cancer
The performance of neural network is largely dependent on their capability to extract very
discriminant features supporting the characterization of abnormalities in the medical image …
discriminant features supporting the characterization of abnormalities in the medical image …
Collaborative consultation doctors model: Unifying cnn and vit for covid-19 diagnostic
The COVID-19 pandemic presents significant challenges due to its high transmissibility and
mortality risk. Traditional diagnostic methods, such as RT-PCR, have limitations that hinder …
mortality risk. Traditional diagnostic methods, such as RT-PCR, have limitations that hinder …
Insights from the COVID-19 Pandemic: A Survey of Data Mining and Beyond
The global health crisis of COVID-19 has ushered in an era of unprecedented data
generation, encompassing the virus's transmission patterns, societal consequences, and …
generation, encompassing the virus's transmission patterns, societal consequences, and …
Hyper‐tuned CNN using EVO technique for efficient biomedical image classification
This research utilizes metaheuristic optimization inspired by the Egyptian Vulture
Optimization (EVO) technique. Biomedical image segregation is developed to reduce the …
Optimization (EVO) technique. Biomedical image segregation is developed to reduce the …
FRNet: A Feature-Rich CNN Architecture to Defend Against Adversarial Attacks
Adversarial attacks that are possible in natural images are also transferable to medical
images, paralyzing the diagnostic process and threatening the robustness of underlying …
images, paralyzing the diagnostic process and threatening the robustness of underlying …