BACKGROUND AND OBJECTIVE: In the traditional diagnostic process for multiple myeloma cancer, a pathologist screens prepared blood samples using a microscope to detect, classify, and count plasma cells. This manual approach is time-consuming, exhausting, and prone to human errors. Consequently, medical experts and researchers are highly interested in any tool that partially or entirely automates this process. To achieve this goal, we developed a software tool called PlasmaCell CAD to analyze effective cells for diagnosing multiple myeloma cancers through microscopic images. METHODS: In the proposed software, to detect and segment cells, we exploit the Mask-RCNN model that has been enhanced by leveraging the circlet transform for the anchor generation. Also, we use the SVM classifier to identify normal and abnormal plasma cells in this software. Moreover, we designed and developed a graphical user interface (GUI) for the PlasmaCell CAD so that users would be able to work with it more easily. RESULTS: we considered the performance of the proposed software on both a publicly available dataset and a locally collected dataset. The experimental results demonstrated the capability and efficiency of PlasmaCell CAD software in segmenting and classifying plasma cells as well as its ease of use. CONCLUSIONS: PlasmaCell CAD is a free software tool that can easily be downloaded and installed on any computers running Windows. PlasmaCell CAD provides a user-friendly GUI with several image processing and visualization facilities for the user that can accelerate the diagnosis process. In light of promising results, PlasmaCell CAD software can be useful to pathologists in helping to diagnose multiple myeloma cancer.