Schahrazad Soltane*, Sameer Alsharif , Salwa M.Serag Eldin
Computer Systems Science and Engineering, Vol.40, No.2, pp. 629-644, 2022, DOI:10.32604/csse.2022.019333
Abstract Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lymphoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma. These Lymphoma types… More >
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