A major obstacle for cancer research is the prediction of liver cancer progression. This
research looked at models that can predict how Hepatocellular carcinoma (HCC) would …

The innovative method was developed by bringing federated learning methods to the
medical field. In this study, the author refined federated learning models with data sets …

Convolutional Neural Networks based automated approaches are vastly utilised to
anticipate and diagnose cancer, saving time and reducing mistakes. Deep Learning CNN …

The availability of datasets pertaining to various fields has increased significantly in the past
decade, but there still exists a problem in getting datasets pertaining to the medical field as …

Deep learning-based medical image analysis is an effective and precise method for
identifying various cancer types. However, due to concerns over patient privacy, sharing …

Artificial intelligence (AI) holds great promise in healthcare as it can significantly advances
disease diagnosis using diverse medical datasets. However, learning generalizable …

This paper presents the use of federated learning (FL) in healthcare to improve the efficiency
and accuracy of medical diagnosis while addressing privacy concerns related to medical …

Deep learning has shown promise in accurate medical image analysis, but challenges
remain. Data privacy concerns hinder the availability of large, high-quality medical datasets …

A chief challenge of deep learning in computer-aided diagnosis is to collect a large
heterogeneous dataset from multiple hospitals for constructing a robust deep learning …

Lung cancer is a leading contributor to cancer-related fatalities worldwide, and
histopathological image analysis plays a critical role in cancer detection by identifying …