Accurate and timely detection and classification of lung abnormalities are crucial for effective
diagnosis and treatment planning. In recent years, Deep Learning (DL) techniques have …

In this study, we propose a novel approach called DCNN-ViT-GRU, which combines deep
Convolutional Neural Networks (CNNs) with Gated Recurrent Units (GRUs) and the Vision …

Lung cancer is a leading cause of cancer death worldwide. The survival rate is generally
higher when this disease is detected in its early stages. Advances in artificial intelligence …

Lung cancer, the second most prevalent and lethal cancer, is caused by aberrant and
uncontrolled cell division in the lungs. Once lung cancer spreads to surrounding tissues or …

The growing dependence on deep learning models for medical diagnosis underscores the
critical need for robust interpretability and transparency to instill trust and ensure responsible …

Cervical cancer ranks as a leading cause of mortality among women, with the conventional
pap smear-based detection posing significant challenges due to its labor-intensive and time …

One of the most dangerous diseases in modern times is generally thought to be lung cancer.
Colon cancer will be the second biggest killer of Americans in 2023 when it comes to …

Abstract Machine learning is an interdisciplinary approach in building mathematical models
from known inputs to make data-driven predictions and decisions. In this project, we provide …