Amjad Rehman1, Muhammad Mujahid1, Robertas Damasevicius2,*, Faten S Alamri3, Tanzila Saba1
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937
- 31 October 2024
Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >
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