Among many type of cancers in human body especially for woman in this modern world, the breast cancer is a key problem for their physical fitness. The early diagnosing of cancer for middle aged women in a major issue among us. In breast cancer detection using the images based on mammography, it's very difficult to detect exact cancer affected area due to the properties of the image. The Mammogram images occupy large size of file due to its high-resolution of graphic images. One can identify the cancer cells and the normal cells with contrast value, but the contrast difference is very thin. To identify and symbolize metastatic tumors in early stages and perform surveillance for most cancers recurrence, the volume of tumor can be measured by the use of Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) and useful characterization through Positron Emission Tomography (PET) imaging. In this article, breast cancer images are taken from medical repository and cancer affected regions are detected by region of interest and the cancer affected area is classified by the Susan technique and the results are tabulated. SUSAN corner detector is a good method which yield high quality features. To use in a real-time application, this is computationally demanding. In short amount of time, this can be used to build a feature detector. This Susan corner detector outperforms current feature detectors by a large margin. © 2024 Elsevier B.V., All rights reserved.