New bag-of-feature for histopathology image classification using reinforced cat swarm algorithm and weighted Gaussian mixture modelling

The development of computer-aided diagnosis for medical image analysis requires an efficient classification method to improve the decision-making process. Pathological images are considered as the gold standard for detecting carcinoma depending upon the observed texture, shape, and color of the cell [1‐3]. Digital pathology enables the examination of stained images for diagnosis and classification of diseases [4‐6]. However, an efficient classification system for histopathological images is still in development and these images are studied manually by pathologists. Histopathology is the study of inflammed tissue section which contains the complex structure having various morphological variabilities [7]. These complex structured images make it difficult to design an efficient and effective automatic classification system [8]. To depict the complexity and variability in histopathological images, some of the representative images are shown in Fig. 1. The observation shows that the manual approach leads to substantial variation [9] and is usually a time-taking process [10]. Therefore, there is a growing requirement for an automated histopathology image classification process for their quantitative analysis [11‐13]. For the same, digital histopathology tissue patterns may be utilized to extract the information and advancing the classification process. The effective classification method may discover the various categories and explore the benign or malignant stages of histopathological images.

There are various methods presented for histopathological image classification. However, bag-of-features (BOF) is one of the successful methods used for histopatholgical images. Furthermore, the appropriate feature selection and visualization of different varieties of features’ descriptors assist in classification. A BOF framework is used for feature selection and feature representation constructively in computer vision [14]. BOF is a popular method in which a collection of gathered features is assembled in clusters through the k-means technique. The visual vocabulary is obtained from extracted feature descriptors using the K-means clustering method. Here, the clustering approach allocates the data vectors to each cluster with minimum cohesion [15]. However, K-means may give biasness towards cluster formation on high-dimensional feature descriptor and generally trap in local optimum. Furthermore, the visual words’ occurrences in each image are represented using the histogram. Various researchers propose the variants of BOF methods for achieving better accuracy [16]. Mittal et al. [17] classified the histopathological tissue images into connective tissue, nervous tissue, epithelium tissue, and muscular tissue. In this method, an improved BOF method is applied by implementing a gravitational search algorithm to acquire optimal visual words. Dimitrovski et al. [18] presented the annotation hierarchy for multi-label classification of the medical images using the predictive clustering tree technique. Cruz-Roa et al. [19] proposed the automatic annotation, consisting of BOF representation, probabilistic annotation, and latent topic model on the histopathological image. The support vector machine (SVM) method is used for classification and evaluating the accuracy, precision, and F-score measures.

The overall contribution of the paper has threefold; (1) a new reinforcement cat swarm optimization (RCSO) is introduced, (2) improved feature representation method, referred to as weighted Gaussian mixture modelling, is presented, and (3) a new BOF model for classification of histopathological images is developed. The RCSO algorithm’s performance is tested on 29 benchmark functions of CEC 2017, and comparative analysis is done on the existing state-of-the-art algorithms, namely lion optimization algorithm (LOA), cat swarm optimization (CSO), whale optimization algorithm (WOA), and particle swarm optimization (PSO). Moreover, rigorous statistical analysis is performed for checking the significance of acquired results. Furthermore, the comparative analysis is performed based on BOF technique, namely PSO_BOF, WOA_BOF, CSO_BOF, and LOA_BOF. The quality metrics measures are evaluated by computing recall, precision, F1-score, and accuracy on different histology dataset categories using SVM and multilayer perceptron (MLP). Furthermore, confusion matrices are generated for each category of benign and malignant histopathological images. The experimental result analysis and observation show that the proposed method is efficient and outperforms the existing state-of-the-art methods.

The rest of the paper is structured in the following sections. Section 2 discusses the literature review and CSO algorithm, Sect. 3 elaborates the proposed methodology, Sect. 4 presents the experimental analysis, and conclusion are drawn in Sect. 5.

Bio-inspired or meta-heuristic algorithms are the nature-inspired techniques that help to solve real-world optimization problems. In literature, several nature-inspired algorithms have been presented which mimic a biological or physical behavioural phenomenon to solve real-world optimization problems [20]. Some of the popular meta-heuristic algorithms are PSO [21], CSO [22], WOA [23], Cuckoo Search (CS) [24], LOA [25], and many more. O’Boyle et al. [26] presented the selection of optimal features subset using ant colony optimization (ACO) algorithm and simultaneously improved the parameter metrics of SVM. Rodrigues et al. [27] demonstrated optimal path forest and bat algorithm for novel feature selection process. Gu et al. [28] introduced the CSO technique to reduce the dimension of the dataset and select the best features. CSO is a popular variant of the particle swarm algorithm for determining the relevant attributes among extracted features. Mittal et al. [29] presented an improved gravitational search algorithm which is a meta-heuristic technique based on Newtonian gravity to determine optimal threshold value in the 2D histogram of an image. Regardless of their nature, population-based meta-heuristic clustering optimization algorithms return possible global optimal value of the desired feature by exploiting and exploring the input search space. Therefore, researchers used the meta-heuristics algorithms in BOF method for achieving better accuracy.

Tiwari et al. [30] depicted the effective BOF method, utilizing whale optimization algorithm and tested it on blue histology dataset. Kumar et al. [31] presented the comparative analysis using bag of visual words method on KIMIA Path960 histopathological images for performing classification. Pal et al. [32] developed enhanced BOF method by contributing SBBO (spiral biogeography-based optimization) meta-heuristics technique to perform classification on histopathology images. The generation of optimal visual words is done using SBBO in BOF. Thus, the optimal cluster center is determined to provide the best solution. The different connective tissues in the histopathological images are classified using SVM. Rachapudi et al. [33] presented a BOF method utilizing a novel random salp swarm algorithm to improve the medical image classification. The comparative analysis is performed for computing quality metrics with five different meta-heuristics based BOF method. Díaz et al. [34] presented an algorithm in which the local region in the stained histopathological image is extracted using SIFT descriptors of stained components and applied probabilistic latent semantic analysis method (pLSA) for classification. Zhang et al. [35] depicted a model based on multi-scale non-sparse coding for medical image classification. The developed method extracts the feature descriptors and produces the histogram. BOF method uses the k-mean algorithm to determine the clusters and forming the visual vocabulary to form the codebook.

Kaushal et al. [36] performed the analysis of different feature extraction and classification technique on BreaKHis dataset. The experimental result are computed on quality metrics parameters, such as F-measure, recall, accuracy, precision, and G-mean. Furthermore, image representation in BOF also plays an important role. Shi et al. [37] presented NLLSC (non-negative factorisation and Laplacian sparse coding) method for image representation and classification. The technique preserves the local information and reduces the uncertainty of sparse coding. However, the different variants [38‐40] of sparse coding usually fail to examine multi-scale layers of image and essential contextual dimension information from different patch regions of the image. Pal et al. [41] introduced the approach for searching key point based on grey relational analysis BOF method. The results present that bag of visual words technique gives better accuracy. Sanchez-Morillo et al. [42] proposed BOF combined with KAZE features to classify malignant and benign on breakHis histopathology images. The extracted key points are outlined with histogram vector utilizing K-mean clustering given as input to a binary SVM classifier. Hashimoto et al. [43] showed the tissue classification of liver H &E-stained histopathology image based on BOF. The observation states that the average classification is increased by applying BOF on selected spectra of RGB histology image. Beagum et al. [44] employed BOF for feature extraction and selection. Furthermore, an SVM is implemented for classification on microscopic images and good accuracy is obtained. Mhala et al. [45] extracted features using the SIFT method and applied BOF on image feature descriptors.

Moreover, Pal et al. [46] presented the four different folds of a BOF technique such as extraction, codebook building, encoding, and classification. The weighted two-dimensional vector quantisation encoding method is contributed in BOF to improve the classification. Cao et al. [47] proposed a method for semantic visual words based on BOVW, applying probabilistic latent semantic analysis. However, the K-means clustering algorithm is used in BOF to obtain visual words. The method traps into local optimum due to biasness towards initial seeds. Thus, the defects of BOVW cannot be adequately solved. Therefore, this paper introduces an enhanced effective BOF method to overcome the limitations and challenges by introducing a new variant of cat swarm optimization technique for clustering the visual words and new weighted Gaussian mixture modelling method for the representation of optimal visual words.

This paper proposes a new BOF approach for efficient classification of H &E-stained histopathological images. A detailed flow graph is presented in Fig. 2. Before applying the new BOF, Gaussian filtering is applied on images for removing the noise and improving the quality. In Gaussian filter, angle consider standard deviation of 0.5. For the feature extraction, a total of 106 statistical, structural, and texture set of features are extracted for effective classification [49]. Here, 19 features from GLCM (gray-level covariance matrices) [50], 7 features from GLRLM (gray-level run length matrices), 4 features from GLDM (gray-level dependency matrix), 59 features from LBP (local binary pattern), 6 features from HOG (histogram of the oriented gradient) [53], and 5 features from SIFT (scale-invariant feature transformation) are extracted. These features help in exploring and visualizing the patterns in histopathology images [54, 55]. In GLCM [56], gray co-matrix function examines the spatial relationship of pixels. GLDM [57] computes the gray-level difference approach probability density function for the given image and determines the feature descriptors. GLRLM evaluates the count of gray-level runs of varying length. In LBP, the image transformation occurs by thresholding each pixel’s neighborhood and processing the result in a binary number. HOG method uses the orientation of localized portions and gradients of pixel values in the image to create the histogram of the regions. SIFT method determines the key points that is invariant to rotation or scale.

The proposed method determines the optimal visual words using reinforcement cat swarm optimization (RCSO) and k-means algorithm for clustering followed by histogram representation for frequency of each visual words in images through weighted Gaussian mixture modelling. The following sections present the RCSO algorithm, RCSO-based clustering method, feature representation method, and classification process of histopathology images.

In this work, the proposed method has been tested on histopatholgical image datasets. The datasets consist of 640 stained tissue histopathological images collected from two separate databases, i.e., ADL (Animal Diagnostic lab), Pennsylvania State University [61] and Break His [62]. The crop-resize histopathological image of dimension 700x490 with 24 bit depth is extracted. The histopathological image datasets comprise images of four organs, namely Spleen, Kidney, Lung, and Breast scanned at 40x optical magnification. Extensive experimental analysis is implemented on python 3.6 using environment configuration as Intel core i7 with 2.2 GHz speed, 16GB RAM, NVIDIA Geforce GTX1080 ti 4GB, and Windows 10 operating system. Series of the simulation were conducted for computing the performance of NBOF. Parameter tuning is performed for generating optimal value using RCSO and is depicted in Table 1. Moreover, Table 2 illustrates the SVM and MLP parameters considered for classification.

Automated histopathological image classification plays a significant role in acquiring quantitative analysis and supports decision-making process. This paper uses different feature extraction methods, namely GLCM, GLRLM, GLDM, LBP, and HOG for gathering the features. Furthermore, the features are given into a new RCSO-based clustering method for determining the optimal visual words in BOF. The frequencies of each optimal visual features are demonstrated in the form of histogram using new weighted Gaussian mixture modelling in BOF. The generated BOF is given to SVM and MLP networks to classify benign or malignant along with different categories of histopathological images. RCSO algorithm provides the optimal solution and is tested on IEEE CEC 2017 benchmark standard functions. The convergence behaviour shows that RCSO provides more prominent results as compared to existing state-of-the-art algorithms. Furthermore, rigorous statistical test results validate the significance of the proposed algorithm. The experimental results of RCSO-BOF are analyzed and compared with PSO-BOF, WOA-BOF, CSO-BOF, and LOA-BOF methods.

In future work, RCSO algorithm may be used to quantify the tissue cells. Moreover, the accuracy and complexity of the classification system may be improved by fine-tuning of the parameters and using parallel algorithms, respectively. Furthermore, the integration of the developed algorithm with human intelligence may be explored for better learning and decision-making. However, such integration should follow the legal procedures for the human safety and liability implications of AI.