Breast cancer remains a significant global health challenge affecting millions of people worldwide. Early detection is crucial for improving treatment outcomes and survival rates. With the rapid advancement of technology, artificial intelligence (AI) has emerged as a transformative tool in medical diagnostics, particularly in breast cancer detection. This review examines how state-of-the-art machine learning (ML) and deep learning (DL) methodologies have revolutionized breast cancer diagnostics. Techniques such as convolutional neural network (CNN), ensemble learning, transfer learning, explainable AI, and federated learning (FL) have been analyzed for their contributions to addressing multi... More >

Lung cancer is predominantly illustrated as the principal cause of cancer-related deaths globally, especially the diagnosis of late stages creates substantial reductions in survival rate. Recent advancements in artificial intelligence (AI) and medical imaging offer promising avenues for early and accurate detection of pulmonary malignancies. This paper introduces an EfficientNetB0 deep learning architecture used for performing multiclass lung cancer detection through computed tomography scan analysis. The EfficientNetB0 framework was validated, trained and tested on six clinically relevant CT scan image types within a publicly accessible Kaggle database. A combination of transfer learning wi... More >