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

Abstract

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.

Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas

“…We used a Gaussian filter, a preprocessing technique, to improve the histopathological WSI images, ensuring efficiency in the next stage. The presence of some artifacts in the next stage of pretreatment may clog an essential fraction of malignant lymphoma cells, making it difficult to extract features from this segment [ 30 ]. In this study, the histopathological images of WSI were optimized using Gaussian and Laplacian filters, which improve image quality and remove unwanted pixels.…”

Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification

“…The concept of wider, deeper, and higher resolution properties of those pre-trained networks giving the network with more filters, more convolution layers and the ability to process the images with larger depth has gained popularity in the field of image processing. Considering those general advantages as well as a few other advantages, such as VGG16 is good at image classification, the effectiveness of model scaling, the proper use of baseline network in EfficientNet B0, and the principle of ResNet50 to build deeper networks and efficiency to obtain number of optimized layers to overcome the vanishing gradient problem, has been the motivation behind this work to design a deep feature fusion strategy for feature selection leading to an effective skin lesion image classification [ 2 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 17 , 18 , 19 , 20 ].…”

“…The computer-assisted dermoscopic image classification has attracted significant research for its potential to timely and accurately diagnose skin lesions [ 4 , 5 , 6 , 7 ]. Scientists, clinicians, analyzers, and experimenters are trying to delve into this area of research to develop models and strategies by exploring artificial intelligence (AI)-, machine learning (ML)-, and deep learning (DL)-based approaches [ 8 , 9 , 10 , 11 ].…”

“…It is evident that DL strategies are being widely used for structure detection by researchers for localization and interpolation of anatomical structures in medical images and to accomplish this task of distinguishing the image features [ 10 , 11 ]. Additionally, the DL methods are highly effective in handling large samples during the training stage, and this network learns valuable representations of the features directly.…”

“…In medial image processing, especially in skin lesion recognition, it is essential to pre-process the image concerning feature selection and feature extraction leading to feature engineering to design an effective and correctly working algorithm [ 12 , 13 , 14 , 15 ]. The evolution of transfer learning and its advantages of saving resources with improved efficiency concerning cost and time-consuming issues have widely used CNN’s pre-trained networks in the image analysis research domain [ 2 , 11 ]. In other words, this transfer learning is an ML-based approach where a pre-trained model is reused and customized to develop a new model for a new dataset.…”