The histopathological diagnosis of breast diseases requires highly trained and experienced experts, and often strains pathologists’ cognitive capabilities. Accurate and timely diagnosis of breast diseases is essential for the appropriate management of the patients.
The paper presents a knowledge base system that uses a combination of rule-based and case-based techniques to achieve the diagnosis. Rule-based systems handle problems with well-defined knowledge bases this limits the flexibility of such system. Case-based reasoning has been adopted to overcome this inherent weakness of rule-based systems by incorporating previous cases in the generation of new cases to improve the performance of the system. The result of this research shows that the system is capable of assisting pathologists in making accurate, consistent and timely diagnoses. The system also aid in eliminating errors of omission that have been viewed as a prominent cause of medical errors. In conclusion this paper investigated the histological features used in the diagnosis of breast diseases and proposed an integrated knowledge base system based on the features.