Densely Convolutional BU-NET Framework for Breast Multi-Organ Cancer Nuclei Segmentation through Histopathological Slides and Classification Using Optimized Features  

作　　者：Amjad Rehman Muhammad Mujahid Robertas Damasevicius Faten S.Alamri Tanzila Saba 

机构地区：[1]Artificial Intelligence&Data Analytics Lab,College of Computer&Information Sciences(CCIS),Prince Sultan University,Riyadh,11586,Saudi Arabia [2]Centre of Real Time Computer Systems,Kaunas University of Technology,Kaunas,LT-51386,Lithuania [3]Department of Mathematical Sciences,College of Science,Princess Nourah bint Abdulrahman University,Riyadh,11671,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2024年第12期2375-2397,共23页工程与科学中的计算机建模（英文）

基　　金：funded by Princess Nourah bint Abdulrahman University and Researchers supporting Project number (PNURSP2024R346),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：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 proposed the densely convolutional Breast U-shaped Network(BU-NET)framework to overcome the mentioned issues.The study employs BU-NET’s spatial and channel attention methods to enhance segmentation processes.The inclusion of residual blocks and skip connections in the BU-NEt architecture enhances the process of extracting features and reconstructing the output.This enhances the robustness of training and convergence processes by reducing the occurrence of vanishing gradients.The primary objective of BU-NEt is to enhance the model’s capacity to acquire and analyze more intricate features,all the while preserving an efficient working representation.The BU-NET experiments demonstrate that the framework achieved 88.7%average accuracy,88.8%F1 score for Multi-Organ Nuclei Segmentation Challenge(MoNuSeg),and 91.2%average accuracy,91.8%average F1 for the triple-negative breast cancer(TNBC)dataset.The framework also achieved 93.92 Area under the ROC Curve(AUC)for TNBC.The results demonstrated that the technology surpasses existing techniques in terms of accuracy and effectiveness in segmentation.Furthermore,it showcases the ability to withstand and recover from different tissue types and diseases,indicating possible uses in medical treatments.The research evaluated the efficacy of the proposed method on diverse histopathological imaging dataset

关 键 词：Breast cancer HISTOPATHOLOGY BU-NET deep learning 

分 类 号：R737.9[医药卫生—肿瘤]