Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images.

Cancer has a disproportionately large influence on the death rate of adults. A patient needs to get a diagnosis of their condition as quickly as is humanly feasible in order to have the greatest chance of surviving their sickness. Skilled medical professionals use medical imaging and other traditional diagnostic methods to search for clues that may indicate the presence of malignant tendencies inside the body.

Learning for early childhood using the IcanDO platform: Breakthroughs for golden age education in Arabic learning.

Early childhood is an age of children whose learning tendencies rely on games, therefore this study aims to describe the IcanDO platform for early childhood in learning Arabic. The research was conducted with a qualitative approach, the data sources were early childhood teachers. Data collected by observation techniques, interviews and documentation studies.

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage.

Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease Detection.

Alzheimer's disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning.