Breast cancer is the most prevalent form of cancer in women, comprising 14 percent of Indian women's cancers. In both rural and urban India, breast cancer seems to be on the rise. A Breast Cancer Statistics 2018 survey [1] revealed a total of 1,62,468 new registered cases, and 87,090 reported deaths in a year. In higher stages of development, the survival of cancer is challenging, with over 50 percent of Indian women living with stage 3 and 4 breast cancer. Post-cancer survival was registered at 60% for breast cancer women in comparison with the U.S. having 80%. The biopsy is a diagnostic procedure commonly used to collect tissue samples of a human subject and analyze the existence or extent of a disease by a pathologist using a microscope. For observation, these tissues are processed and stained. Optical coherence tomography (OCT) provides an innovative non-invasive modality of optical imaging that can provide three-dimensional, high-resolution images of biological tissue structures omitting the requirement of the staining process [2]. The classification of breast tissue types with optical coherence microscopy (OCM) can be improved further by incorporating texture analysis. The combination of OCT and confocal microscopy have a better depth of penetration than confocal microscopy and higher resolution than OCT [3].

Zhou et al. [4] developed an integrated system of OCT and OCM in their laboratory. They presented a comparative analysis of corresponding histologic sections with this integrated imaging for the identification of distinguishing features or characteristics to differentiate benign and malignant breast lesions at various resolutions. Fig. (1) shows the features observed by the authors in normal breast tissue and malignant tissue for both OCM/OCT images and stained images.

Fig. (1). (Top) Normal tissues image and its features (bottom) Malignant (i.e., invasive carcinoma) tissues image (adopted from previous research [4]).

Fig. (1) shows that the apparent curved hypo scattering monomorphous circular structures in adipose tissue using OCT/OCM image is clearly visible in normal tissue. The next image in Fig. (1) shows the observed feature in benign tissue in which different sizes of necrotic (hyposcattering) adipocytes are visible clearly in the OCT image. This work demonstrates that texture pattern plays an important role in tissue classification.

Texture analysis has been used in many biomedical applications [5-7]. This texture analysis method can be categorized into four classes: (a) statistical approaches, (b) structural approaches, (c) Model approaches, and (d) Transformation approaches. Statistical approaches include a Gray level co-occurrence matrix (GLCM), local binary pattern (LBP), auto-correlation function (ACF), and histograms feature [8]. The morphological operations [9], primitive measurements [10], and skeleton representation [11] are the subpart of the structure-based texture analysis. The auto-regressive model and random field generation [12] are the subclasses of model-based texture representation. Wavelet transform [13], Gabor transform [14], curvelet transform [15] etc analyzes the texture information using transformation methods. Statistical approaches are versatile in contrast to structure-based strategies. They do not include pictures that include repeated patterns of form and are better adapted to the study of OCT / OCM tissue specimens.

More recently, the use of a hierarchical training framework in deep learning is the most robust approach for feature extraction and classification in computer vision applications. Many deep learning approaches have been used in the human breast tissue classification yielding excellent performance, which is further discussed in Section 2.

A local binary pattern having robust discrimination capabilities can reduce the burden of CNN and can enhance the performance of CNN for better classification [16]. Therefore, this work represents the breast cancer image classification using the integrated approach of texture pattern analysis and deep learning. To take advantage of both spectral and spatial features, a deep CNN architecture using local binary pattern-based texture features is proposed in this paper. We followed the algorithm proposed in earlier work [17]. The CNN architecture is trained from scratch using the BreaKHis dataset [18] for binary classification. LBP generally works with gray-scale images. This approach offers the advantages of significant parameter saving, fast convergence, and avoids overfitting problems.

The overall structure of the paper is as follows: Section 2 introduces works of literature on histopathological image classification. The survey is divided into two parts: (a) texture features based classification models and (b) various deep learning approaches used in the breast cancer image classification. Section -3 introduces LBP and CNN structure to use these LBP features. Experimental analysis and results are discussed in section 4. At last, concluding remarks are presented in section 5.

Histopathology images demonstrate the effect of the influence of diseases through the underline tissue structure, which is usually preserved during staining image processing. The structure analysis is a crucial step in the diagnosis process in the detection and classification of these histopathological images. Previously, segmentation approaches were used for the classification of images [18]. Later region of interest was identified using active contour formulation [19]. The technical advancement in computer-based vision processing leads to various alternates in the analysis of histopathological images. The wavelet transform based texture analysis was used [20, 21] to detect lung cancer. Gabor filter provides texture characterization at various scales, and orientation also offers good advantages in breast cancer detection and classification [22]. GLCM based texture classification was also demonstrated for prostate and skin cancer detection [23]. A local structure tensor was also used to extract the texture information and image segmentation [24]. Vast CNN models are presented in the literature. However, we limit our study to LBP and LBP integrated CNN networks. This section initially presents the use of local binary pattern in cancer image classification, and later use of CNN and LBP in histopathological images are presented.

As discussed above, most existing algorithms have used either the deep CNN (DNN) algorithm or handcrafted texture features in the detection and classification model. Individually both models have shown promising results. LBP has provided competitive results to deep CNN-based methods, particularly in the presence of rotations and noises [43]. However, to reduce the burden of CNN while maintaining the performance of the system, we presented an alternate way of fusion of the handcrafted texture features with deep learning architectures.

The local binary pattern is a powerful operator to discriminate texture features among all texture feature operators. It provides rotation and translation invariant features. In an image plane I(x,y), the LBP operator finds a similarity between the neighbor pixels over predefined block size. Let (Xc, Yc) be the center of the block with the size PxP. Then LBP is obtained by comparing center pixels with its P²-1 neighbor pixels residing in the circle of radius R. It can be expressed mathematically as,

(1)

Where s (·) = 1 if f (x_p, y_p ) ≥  f (x_c, y_c ) otherwise s (·) = 0. To generalize the description of LBP, few variations are as follows: LBP is generally encoded using a base of two. Thus weights assigned to each bit are guarded to the power of two. It can be generalized by allowing to take any real value by the weights. Variation in the texture pattern is encoded with the center pixel as a pivot for linking the neighbor pixel’s intensity within the block. The change in consideration of center pixels allows the encoding of different types of texture pattern. In LBP code formation, the specific order of pixels, i.e., anti-clockwise, is used to preserve the pattern information. The change in the order of the pixels causes variation in the generated code for the given block. Thus the configuration of these three parameters determines the encoding of the local texture pattern. Juefei-Xu et al. [17] formulated these LBP operations using the convolution operation. They convolved images with eight 3x3 convolution filters and performed binarization operations

to obtain the same results as obtained by LBP coding. All eight convolutional filters can be expressed as a 2-sparse difference filter. These filters are shown in Fig. (2). In standard LBP, all bits are weighted by the power of two and then accumulated to get the code of the respective block. Therefore, the convolution formulation of LBP can be expressed mathematically as,

(2)

Where f (x, y) represents the convolution filters as shown in Fig. (2) for 8 neighbors, and H is the Heaviside function for thresholding operation. This formulation is used as a basis of CNN architecture in the proposed implementation. In the deep CNN architecture, a vector of an image is convolved with a filter followed by a non-linear like Rectified Linear Unit (ReLU), sigmoid, etc. The same operation is also obtained in LBP directed CNN architecture where convolution filters are used, as expressed in Fig. (2), and the Heaviside function is used as a non-linear operator. Secondly, basic LBP is operated on grayscale images. To fully utilize the information present in each color channel, we proposed the fusion of LBP code over all the color channels as expressed earlier [44]. The formulated LBP operator uses three-color maps, and fusion of these feature maps is used at every layer of the CNN architecture, as shown in Fig. (3).

The main advantage of the convolution formulation of LBP is that filters used in operation have a size of 1x1. Let “l and k” represent input and output feature maps respectively. The size of the convolutional filter is “n*m”. If p is intermediate channels, the ratio of the required parameters for standard CNN and LBP formulated CNN can be expressed by equation 3 [17]. If input feature map p is set equal to intermediate LBP channel m, then ration reduces to n × m. Thus it reduces the burden of CNN by a factor of 9 for 3x3 filter size. For detailed analysis, the readers are requested to refer to the paper that discusses it [17].

Fig. (2). LBP Coding formulation using eight convolution filters.

Fig. (3). Multi-channel Local binary pattern encoding and its fusion.

(3)

The local binary pattern framework in CNN is shown in Fig. (4). Fig. (4) shows the formulated layer structure using LBP filter, i.e., non-trainable filter and 1x1 convolutional filter, i.e., trainable filter. The proposed algorithm is tested on the BreakHis dataset for binary classification in benign and malignant tissue. Section 4 presents the experimental analysis.

We configure the architecture of CNN to use LBP features and compare the obtained results with state-of-the-art methods. We consider the dataset of BreaKHis containing 2480 benign images and 5429 malignant images. All images are captured at four magnification factors, i.e., 40x, 100x, 200x, and 400x. All images have an RGB color map with a 700 x 460 resolution. To avoid overfitting problems, training images have been increased by the augmentation process using rotation, shifting, and mirroring. This augmentation process has been adopted in many CNN algorithms [45]. The model is based on the ResNet network. The filter size is fixed to 3x3 to incorporate LBP features formation in the first layer. The ReLU activation function is used after the first layer providing an LBP map and offering faster convergence in comparison with the Sigmoid function. The second layer contains trainable parameters with a size of 1x1 providing the feature map. The conceptual architecture of LBP directed CNN is shown in Fig. (5). Total 20 local binary pattern directed convolutional layers with filters of size 3 x 3 were used in the experiment. The regular CNN architecture with 20 layers requires approximately ~10.52 million learnable parameters, whereas LBP directed CNN architecture requires ~1.56 million parameters. Thus, the use of fixed LBP filters in CNN architecture reduces the parameters by 6.72 factor.

The dataset is divided into 70% and 30% for training and testing the network. All images are resized to 512 x 512. The network is trained using 150 epochs with 20 batch size. Received training accuracy and the testing accuracy is shown in Table 1 and Figs. (6 and 8). Table 1 demonstrates that the algorithm achieves higher accuracy for the 200x magnification factor in comparison with other magnification factors. Eventually, the maximum testing accuracy achieved with 200x magnification is 96.92% in comparison with training accuracy, i.e., 96.46%. From Figs. (6 and 8), it has been observed that the increment in the training and test accuracy is moderate after the 75 epochs. The receiver operating characteristic (ROC) gives the performance of the network at the various thresholds in terms of true positive rate versus false-positive rate. The area under the ROC graph (AUC) provides comprehensive performance measurement. The proposed network obtained more than 98% AUC. Further accuracy and loss analysis for both training and testing images are presented in Figs. (7 and 9).

Fig. (4). Conceptual flow of Local binary pattern directed convolutional layer.

Fig. (5). The CNN network using LBP features.

Fig. (6). Training Accuracy vs. Epoch for BreaKHis dataset.

Fig. (7). Training Loss vs. epoch for BreakHis dataset.

Fig. (8). Test Accuracy vs. Epoch for BreaKHis dataset.

Fig. (9). Test loss vs. Epoch for BreaKHis dataset.

As classification accuracy evaluation does not provide the complete information, balanced information between the precision and recall, a measurement of classifier exactness and classifier completeness are equally important. Therefore, F1-Score giving a balance between precision and recall is demonstrated in Table 1.

The combination of machine learning algorithms with features of LBP is not common in literature. Table 2 summarizes the finding on the BreaKHis dataset of histopathological images. Basic architecture like LeNET and ALEXNET architecture was able to succeed with 72% and 95.70% accuracy, respectively. The performance of the class structure-based deep convolutional neural network (CSDCNN) [46] is better than traditional networks with an average accuracy of 93.25% in the presence of a large number of parameters due to deep structure. In another research [34], the author tested VGG16 and ALEXNET network with LBP features. The feature length is quite high, i.e., 4096. VGG16 was speciously superior than ALEXNET with an accuracy of 94.72%. In their proposed approach, LBP features were classified using histogram intersection kernel-based SVM (IKSVM). All images are resized to 512 x 512 in the process, and obtained accuracy is 96.50. The direct comparison cannot be established because they have used the SVM approach and the dataset is also different. However, the proposed algorithm’s accuracy is similar with a reduction in the training parameters by the factor of 9. Bayesian network [31] has been tested on MIAS datasets consisting of gray-scale mammogram images with accuracy limited to 92%. Spanhol et al. [47] also proposed a CNN network with maximum accuracy of 90% achieved on the dataset with a 40x magnification factor. However, for another magnification factor, the accuracy level is limited to 88.40% only, limiting the average accuracy to 87.27%. The fusion of VGGNET and Random Forest (RF) was also used [48] for binary classification of BreaKHIS dataset and achieved 93.41% accuracy. A multi-task exception CNN was presented with fine tuning of the hyperparameters [49]. The performance of their network is better than the single task CNN with 93.36% of accuracy. The ALEXNET CNN architecture has 2.33 million learnable parameters [17, 50] and VGGNET16 architecture has 15 million learnable parameters. The proposed network achieved 96.46% on images with a 200x magnification factor with 1.5 million learnable parameters. Table 3 summarizes the required learnable parameters by each model.

In this paper, we demonstrated the use of LBP features in the CNN network. The formulation of LBP using eight convolution filters makes the model simpler with fewer learning parameters. The study proposes that texture features play a significant role in the histopathological image classification and LBP generates the most robust rotation and illumination invariant features in comparison with other texture features operators. Recent deep CNN architectures have achieved comparable classification accuracy at the cost of high training parameters or computational complexity. Therefore, the suggested alternates reduced the burden of CNN architecture and preserved the accuracy level. This makes it suitable for the environment having fewer resources. The proposed algorithm obtained an average accuracy of 96.06% on the BreaKHis dataset. This validated the proposed network’s performance. The performance achieved can be further tested on other datasets as well as mammogram images.