Lung cancer is a disease that affects and causes abnormalities in the lungs. The current
methods to find and treat lung cancer require precise and timely detection to improve patient …

Lung cancer is one of the most common cancers worldwide. In 2020, there were an
estimated 2.2 million new cases of lung cancer. It is often diagnosed at an advanced stage …

Access to a large dataset is necessary to improve disease detection with excellent accuracy.
However, due to data confidentiality and privacy restrictions, collecting data from hospitals or …

This study distrubuted learning and machine learning to diagnose lung illnesses, is
described in this research report. The experimental design examines four different …

Lung cancer is characterized by high mortality and incidence rates, making it one of the most
prevalent cancers globally. Early detection significantly improves the chances of survival for …

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

Sophisticated diagnostic techniques are required for effective and early identification of lung
cancer, a worldwide health problem. This study explores the creative use of federated …

In the era of big data, Federated Learning has emerged as a promising way of learning from
scattered nodes. In its essence, Federated Learning is a distributed learning methodology …

Deep learning paradigms, particularly Convolutional Neural Networks (CNNs), have carved
out a place in imaging-based illness identification in the modern age of medical diagnostics …

Machine learning is increasingly significant in health science because it can infer valuable
information from high-dimensional data. However, combining research and patient data from …