Precision oncology relies on the identification of targetable molecular alterations in tumor
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …

Carcinoma is a primary source of morbidity in women globally, with metastatic disease
accounting for most deaths. Its early discovery and diagnosis may significantly increase the …

Breast cancer, the earliest documented cancer in history, stands as a foremost cause of
mortality, accounting for 684,996 deaths globally in 2020 (15.5% of all female cancer cases) …

The deep learning models such as AlexNet, VGG, and ResNet achieved a good
performance in classifying the breast cancer histopathological images in BreakHis dataset …

In medical imaging, the effective detection and classification of Breast Cancer (BC) is a
current research important task because of the still existing difficulty to distinguish …

Neuroblastoma is the most common extracranial solid tumor in early childhood. International
Neuroblastoma Pathology Classification (INPC) is a commonly used classification system …

The aptness of machine learning (ML) to learn from large datasets, discover trends, and
make predictions has demonstrated its potential to metamorphose the medical field. Medical …

Background. Type 2 diabetes is a major health concern worldwide. The present study is
aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes …

Evolutionary Algorithms (EAs) play a crucial role in the architectural configuration and
training of Artificial Deep Neural Networks (DNNs), a process known as neuroevolution …

Breast cancer is one of the most often diagnosed cancers in women, and identifying breast
cancer histological images is an essential challenge in automated pathology analysis …