The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …

Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …

Recently, ConvNeXts constructing from standard ConvNet modules has produced
competitive performance in various image applications. In this paper, an efficient model …

Medical data are present in large amount and this is difficult to process for the diagnosis and
Healthcare organization requires effective technique to handle big data. Existing techniques …

Histopathological image recognition of breast cancer is an onerous task. Although many
deep learning models have achieved good classification results on histopathological image …

Convolutional neural network (CNN) has promoted the development of diagnosis
technology of medical images. However, the performance of CNN is limited by insufficient …

One of the most critical steps in medical health is the proper diagnosis of the disease.
Dermatology is one of the most volatile and challenging fields in terms of diagnosis …

Melanoma is the most fatal type of skin cancer which can cause the death of victims at the
advanced stage. Extensive work has been presented by the researcher on computer vision …

Background and objective: Deep learning-based methods for fast target segmentation of
magnetic resonance imaging (MRI) have become increasingly popular in recent years …

Deep learning models, particularly transformers and convolutional neural networks (CNNs),
have been commonly used to achieve high classification accuracy for image data. Since …