Breast cancer detection Artificial intelligence and early diagnosis. The incidence of breast cancer, among all the cancers affecting women worldwide, makes it the most prevalent one. Therefore, it causes many death. Early detection is the most important for better recovery.

Recently, Artificial Intelligence (AI), ML, and DL approaches are extensively utilized to assist clinicians with the accuracy and effectiveness of detecting breast cancer and other relevant health problems.

Breast cancers can be recognized by analysis performed on many types of image, including MRI, CT scan, X-rays, PET-CT scans and Ultrasound. The ability of AI models to interpret complex medical images accurately makes the diagnostic process quicker and reliable while simultaneously reducing diagnostic errors. In this research review, we introduce and cover the artificial intelligence approach toward breast cancer detection with the application of various types of algorithms such as, but not limited to, the Supervised Learning algorithms; Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression, and Random Forest and the Deep Learning algorithms such as, the Convolutional Neural Network (CNN) &Transfer Learning (TL). This paper outlines used datasets ;Image preprocessing and Feature extraction in order to classify whether the image contains tumor or not, we will look at various performance evaluation metrics ; Accuracy (Acc), Precision (Pre), Recall (Rec) and F1 score (FS) which measure model performance by relating predicted results with actually labels and as well as Area under the Curve (AUC).

We cover also, pros and cons, issues and open questions in applying artificial intelligent systems to diagnostic imaging and some future studies which may lead to more efficient and interpret able systems which can help a great deal in saving many lives