TL;DR
The combination of deep learning with XAI gives an interpretable, high-performance approach for early cancer diagnosis, which can benefit doctors in making better informed diagnostic judgements, improving patient outcomes.
The proposed system offers an efficient, accurate, and interpretable decision-support tool that has the potential to assist pathologists in clinical breast cancer diagnosis while promoting trust in AIdriven healthcare applications.
Globally, the mortality rate caused by lung cancer is rising than any other type of neoplasm. To detect and identify the lung cancer early artificial intelligence could be utilized. This research, therefore, proposes a LungXAI framework that combines the use of a fine-tuned CNN model with Grad-CAM and Retrieval Augment...
A framework that showcases both deep and machine learning models will offer insight into the effectiveness of incorporating artificial intelligence in the screen accuracy of lung cancer classification, and an integrated risk-scoring framework that combines the imaging and clinical pipelines into a single interpretable...
Although performance remains below clinical deployment thresholds, the results support further development of ViT-based triage systems to flag high-risk patients for earlier evaluation and demonstrate the potential of ViTs for early lung cancer risk prediction from routine chest X-rays.
Lung and colon (LC) cancer is the leading cause of death from cancer worldwide. Early detection is key in this disease, which at the same time proves to be a challenge because, in its early stages, symptoms are few. Present a method that offers a comprehensive approach using machine learning (ML) and deep learning (DL)...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
