We summarize here below the cornerstone of Medical Devices risk classification, so that every manufac...
medical image analysis for disease detection can be performed with minimal errors and losses. A survey of deep learning-based medical image classification is presented in this paper.
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We also use optional cookies for advertising, personalisation of content, usage analysis, and social media. By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with...
Cancer is the foremost cause of global mortality that presents a fearsome challenge for both researchers and medical practitioners1. The intricacies of combating cancer lie in addressing abnormal cell growth that originates from various organs, where the unchecked proliferation of cells becomes inherently...
Due to the specificity of medical datasets, problems such as sample imbalance, low data quality, and dataset scarcity can occur. The datasets used in this study were derived from two publicly available pneumonia datasets. The first was the COVID-19 Radiography Database developed by Chowdhury et ...
Medical assessment of activation patterns is recently performed on EMG measurements. EMG can extract the electrophysiological parameters of PSD tremor and ET and generate two types of activity patterns, such as asynchronous and synchronous burst transfer. In ET patients, synchronous patterns are observed...
This work is the first to explore the use of gradient based meta-learning for multiple modalities of medical images Conclusion In this study, we proposed a MetaMed approach that relies on meta-learning by formulating the medical image classification for low data regime as a few-shot learning ...
Moreover, CNNs frequently show sensitivity to changes in the resolution of the input images, which might impact performance when handling scale variations. Since medical images exhibit variations in resolution and texture due to differences in capturing devices, CNNs fail to generalize in this ...
In conventional works, various medical image processing techniques have been developed for accurately classifying the types of breast cancer. Still, it confronts difficulties due to the aspects of increased complexity in computations, error values, false positives, and misclassification outputs. Hence, ...