Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on Histopathological Images

• Published in: Cancers Vol. 14; no. 11; p. 2770
• Main Authors: Shankar, K., Dutta, Ashit Kumar, Kumar, Sachin, Joshi, Gyanendra Prasad, Doo, Ill Chul
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.06.2022; MDPI
• Subjects: Algorithms; Breast cancer; Classification; Decision making; Deep learning; Diagnosis; Histopathology; Image processing; Mammography; Neural networks; Noise; Public health; Transfer learning; histopathological images; deep learning; breast cancer; computer aided diagnosis; cancer; medical imaging
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers14112770

Breast cancer is the major cause behind the death of women worldwide and is responsible for several deaths each year. Even though there are several means to identify breast cancer, histopathological diagnosis is now considered the gold standard in the diagnosis of cancer. However, the difficulty of histopathological image and the rapid rise in workload render this process time-consuming, and the outcomes might be subjected to pathologists’ subjectivity. Hence, the development of a precise and automatic histopathological image analysis method is essential for the field. Recently, the deep learning method for breast cancer pathological image classification has made significant progress, which has become mainstream in this field. This study introduces a novel chaotic sparrow search algorithm with a deep transfer learning-enabled breast cancer classification (CSSADTL-BCC) model on histopathological images. The presented CSSADTL-BCC model mainly focused on the recognition and classification of breast cancer. To accomplish this, the CSSADTL-BCC model primarily applies the Gaussian filtering (GF) approach to eradicate the occurrence of noise. In addition, a MixNet-based feature extraction model is employed to generate a useful set of feature vectors. Moreover, a stacked gated recurrent unit (SGRU) classification approach is exploited to allot class labels. Furthermore, CSSA is applied to optimally modify the hyperparameters involved in the SGRU model. None of the earlier works have utilized the hyperparameter-tuned SGRU model for breast cancer classification on HIs. The design of the CSSA for optimal hyperparameter tuning of the SGRU model demonstrates the novelty of the work. The performance validation of the CSSADTL-BCC model is tested by a benchmark dataset, and the results reported the superior execution of the CSSADTL-BCC model over recent state-of-the-art approaches.