International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 7 , Issue 1 , PP: 01-16, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ali Allouf 1 *
Doi: https://doi.org/10.54216/IJAACI.070101
A Decision Support System (DSS) for the recognition of mitotic nuclei (MN) on the histopathological image (HI) aids pathologists in cancer diagnoses by automating the MN detection, a key indicator of tumor proliferation and cell division. Leveraging innovative image processing and machine learning (ML) algorithms, such a system can accurately detect MN, which are crucial indicators of cell division and tumor proliferation. By automating these processes, pathologists can focus more on complicated diagnostic tasks while ensuring efficient and consistent analysis. ML approaches, comprising support vector machines (SVMs) or convolutional neural networks (CNNs) can be widely applied for the classification task. These techniques learn from annotated data to accurately discriminate between mitotic and non-MN. Incorporating these technologies into pathology workflow facilitates research efforts in oncology for improved treatment strategies, enhances diagnostic accuracy, and reduces variability among observers. This study presents an Optimal Bayesian Neural Network based Decision Support System for Mitotic Nuclei Detection (OBNN-DSSMND) technique on Histopathologic Imaging. The goal of the OBNN-DSSMND technique is to detect the mitotic and non-mitotic cells on the HIs. In the initial phase, the OBNN-DSSMND technique undergoes the bilateral filtering (BF) technique to preprocess the input images. Next, the OBNN-DSSMND technique involves a feature fusion process encompassing SqueezeNet, DenseNet, and VGG-19 models. Meanwhile, the hyperparameter selection of the DL models is performed by using the Archimedes Optimization algorithm (AOA). For mitotic nuclei detection, the OBNN-DSSMND technique applies a BNN classifier, which recognizes the presence of mitotic and non-mitotic cells on the HIs. The experimental assessment of the OBNN-DSSMND approach was examined utilizing a benchmark image dataset. The widespread simulation analysis reported that the OBNN-DSSMND technique achieves better results than other techniques.
Breast Cancer , Mitotic Nuclei Detection , Decision Support System , Bayesian Neural Network , Histopathologic Image
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