Adam golden search optimization enabled DCNN for classification of breast cancer using histopathological image

• Published in: Biomedical signal processing and control 2024-08, Vol.94, p.106239, Article 106239
• Main Authors: Suganthi, N, Kotagiri, Srividya, Thirupurasundari, DR, Vimala, S
• Format: Article
• Language: English
• Subjects: Adam Optimizer; Breast Cancer (BC); Deep Convolutional Neural Network (DCNN); Deep Learning (DL); Golden Search Optimization (GSO)
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2024.106239

•BC classification using the proposed Adam Golden Search Optimization-based Deep Convolutional Neural Network (AGSO-DCNN).•Pre-processing and segmentation are done by the Gaussian filter and the k-means clustering, respectively.•Then, features such as shape features, statistical features, LVP, and PHOG are extracted.•At last, BC classification is done by AGSO-based DCNN, where AGSO is the integration of Adam Optimizer and GSO algorithm.•The proposed technique attained 97.90% accuracy, 98.00% TPR and 98.30% TNR. Breast Cancer (BC) is a killing disorder, every year it kills millions of human beings. Early diagnosis is the only way to mitigate the mortality rate. Among all kinds of screening methods, medical imaging is an essential method for screening BC. Existing medical imaging alters the tissue structure and cell morphology. To overcome these limitations, histopathology image is used because it can support the decision of pathologists about the closeness or the non-appearance of a disease, as well as it can help in infection development estimation. Hence, this research develops an efficient method for BC classification using the proposed Adam Golden Search Optimization-based Deep Convolutional Neural Network (AGSO-DCNN). Initially, Gaussian filter-enabled pre-processing is utilized for mitigating the noises composed in the input images. Afterwards, k-means clustering is used to feed the input images into the segmentation phase to reduce the complexity of the image. Then, to extract features like shape features, statistical features, Local Vector Patterns (LVP), and Pyramid Histogram of Oriented Gradients (PHOG) feature extraction is performed. Thereafter, the obtained features are forwarded to the multi-grade BC classification stage, where DCNN is employed for classifying the image into six categories, such as apoptosis, tubule, mitosis, non-tubule, tumour nuclei, and non-tumor nuclei. DCNN is trained by the formulated AGSO mechanism, which is obtained by incorporating the Adam Optimizer and Golden Search Optimization (GSO) algorithm. Moreover, the AGSO-based DCNN technique achieved better accuracy, TPR and TNR with the values of 97.90%, 98.00%, and 98.30%, respectively.