Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network

A cancer diagnosis is one of the most difficult medical challenges. Leukemia a type that affects bone marrow and/or blood and accounts for approximately 8% all cancers. Understanding epidemiology trends leukemia critical planning. Specialists diagnose using morphological analysis, but there possibility error in diagnosis. Since so to diagnose, intelligent methods are required. The primary goal this study develop novel method extracting features hierarchically accurately, order various types acute leukemia. This distinguishes between types, namely Acute Lymphocytic (ALL) Myeloid (AML), by distinguishing lymphocytes from monocytes. images used obtained Shahid Ghazi Tabatabai Oncology Center Tabriz. type-II fuzzy deep network designed purpose. proposed model has an accuracy 98.8% F1-score 98.9%, respectively. results show high diagnostic performance. Furthermore, ability generalize more satisfactorily stronger learning performance than other methods.