Hybrid DSSCS and convolutional neural network for peripheral blood cell recognition system

IET Image ProcessingVolume 14, Issue 17 p. 4450-4460 Research ArticleFree Access Hybrid DSSCS and convolutional neural network for peripheral blood cell recognition system Shivani Joshi, Corresponding Author Shivani Joshi shivani1275@gmail.com Department of Computer Science & Engineering, GL Bajaj Institute of Technology & Management, Greater Noida, Lucknow, UP, India Dr. A.P.J. Abdul Kalam Technical University, Lucknow, UP, IndiaSearch for more papers by this authorRajiv Kumar, Rajiv Kumar Department of Computer Science & Engineering, GL Bajaj Institute of Technology & Management, Greater Noida, Lucknow, UP, India Dr. A.P.J. Abdul Kalam Technical University, Lucknow, UP, IndiaSearch for more papers by this authorAvinash Dwivedi, Avinash Dwivedi Department of Computer Science & Engineering, JIMS Engineering Management and Technical Campus, Noida, UP, 201303 India Guru Gobind Singh Indraprastha University, Delhi, IndiaSearch for more papers by this author Shivani Joshi, Corresponding Author Shivani Joshi shivani1275@gmail.com Department of Computer Science & Engineering, GL Bajaj Institute of Technology & Management, Greater Noida, Lucknow, UP, India Dr. A.P.J. Abdul Kalam Technical University, Lucknow, UP, IndiaSearch for more papers by this authorRajiv Kumar, Rajiv Kumar Department of Computer Science & Engineering, GL Bajaj Institute of Technology & Management, Greater Noida, Lucknow, UP, India Dr. A.P.J. Abdul Kalam Technical University, Lucknow, UP, IndiaSearch for more papers by this authorAvinash Dwivedi, Avinash Dwivedi Department of Computer Science & Engineering, JIMS Engineering Management and Technical Campus, Noida, UP, 201303 India Guru Gobind Singh Indraprastha University, Delhi, IndiaSearch for more papers by this author First published: 11 March 2021 https://doi.org/10.1049/iet-ipr.2020.0370AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract In this study, an efficient a deep learning architecture-based peripheral blood cell image recognition and classification is proposed using hybrid disruption-based salp-swarm and cat swarm (DSSCS)-based optimized convolutional neural networks (DSSCSCNNs) method. The DSSCSCNN method is employed to overcome the hyperparameter problem in CNN and it also helps this model to work on small peripheral blood cell data sets. In the DSSCSCNN method, the authors develop a binary coding technique that converts parameter tuning problems into an optimization problem. The original salp swarm algorithm is enhanced using a disruptive operator and salp swarm optimization algorithm to form the novel DSSCS algorithm which increases the diversity of the search space by providing higher classification accuracy. In this study, the CNNs use Vgg-16 architecture is used for training purposes. The global classification accuracy obtained when trained with the Vgg-16 model is 97%. This method establishes a fine-tuning process to develop a classifier trained using 15,976 images acquired from clinical practice. The proposed model gives improved performance in terms of accuracy, specificity, and sensitivity. In the WBC determination, the proposed approach has shown 100% achievement. It also provides the best overall classification accuracy of 99%. 1 Introduction Peripheral blood cells are fluids that circulate through the blood vessels which is composed of three main types of plasma cells: erythrocytes (red blood cells (RBCs)), leukocytes (white blood cells (WBCs)), and platelets (thrombocytes) [1]. These are essential blood cells that have been used for identifying diseases such as leukaemia, anaemia, cancer, and other infectious diseases to battle viruses. The white blood cells are categorised into five classes, namely eosinophils, basophils, neutrophils, monocytes, and lymphocytes [2]. One should examine the variation of leukocytes in order to classify a blood disease. Leukaemia is a special form of cancer that produces such results in peripheral blood cells with an increasing number of WBCs in the bone marrow. These are caused by the cells, which develop in an uncontrolled discipline and are unable to reach the maturity level [3]. The peripheral blood smears recognise the description and automatic identification of the cell images. The red blood cells are the most important for this study, and this mainly depends on malicious lymphoid cells and also on blast cells. This assists in the clarification of a few morphological characteristics [4, 5] and this help to achieve the effective, quick morphological analysis of the blood cells. The motorised microscope uses the analysers to obtain the methods that attain the pre-classification representation of the blood cell. The classification methods usually involve different steps, such as segmentation, feature extraction, and image classification using pattern realisation methods [6, 7]. A stepwise averaging strategy is used in leukocyte segmentation to separate the nucleus from the cytoplasm. During this process, the fuzzy divergence is decreased, which relies on the fuzzy interval sets [8] and is somewhat superior to other fuzzy sets. The trained classifier support vector machine (SVM) is employed to categorise every image pixel, it also attains the exact segmentation outcome, and this gives better accuracy [9]. In blood smear images, the hybrid entropy approach is used to separate the statistic features and image levels from the segments to detect leukaemia with the highest accuracy [10]. This approach uses a chronological sine cosine algorithm-based actor–critic neural network (Chrono-SCA-ACNN) for classification. The Hough transforms and morphological tools are used to identify three kinds of anaemia-infected RBCs. This method achieves high accuracy with the maximum detection, effectiveness rates by using neural networks (NNs) [11]. The pre-classification system [4, 5] uses expensive image acquisition procedures and algorithms. These complex procedures are not pocket-friendly nor more accurate. The complex morphology present in the peripheral blood cells often reduces the accuracy of the results when tested with the traditional computer vision techniques. The convolutional NN (CNN) overcomes these problems that exist in the state-of-the-art system by using an unsupervised learning approach. It mainly classifies the wide range of blood cells by learning the features from the data using machine learning algorithms. CNN does not use the complicated segmentation procedure nor the costlier image acquisition method which improves the accuracy and leads to a cost-efficient approach for peripheral blood cell image classification. The evolutionary computation techniques [12-15] tends to be quite effective in solving various complex engineering problems and it has been widely utilized by different researchers [15-17] in the last decade. In this study, to enhance the accuracy and tune the hyperparameter problem in CNN, a hybrid disruption-based salp-swarm and cat swarm (DSSCS) optimisation algorithm is developed. The DSSCS algorithm is the hybridisation of the cat swarm algorithm and salp swarm algorithm (SSA) using a disruption operator. The salp swarm optimisation algorithm is mainly used for feature selection which can reduce the complexity of high-dimensional images used in the experiment for classification. The SSA is often trapped under the local optimal solution and offers low convergence. To overcome this problem, the cat swarm optimisation (CSO) is used. The cat swarm algorithm offers significant global search optimisation and fast convergence speed but it sometimes suffers from local entrapment. A disruptive operator is used to fix the issues that exist within these two algorithms. The hybridisation process accurately identifies the changes in the nucleus shape and chromatin clumping in the peripheral blood cell images. The CNN-based DSSCS technique is used for the recognition and classification of peripheral blood cells. The proposed methodology has been shown to achieve 100% achievement in WBC determination and achieves the best overall classification accuracy of 99%. The contribution of this research summarised as follows: The DSSCS optimisation algorithm is emphasised to tune the hyperparameter adjustment problem in CNN. In the DSSCSCNN method, we developed a binary coding technique that converts parameter tuning problems into an optimization problem. To accurately identify the different peripheral blood cell classes utilizing the different layers of CNN. The remainder of this paper is structured accordingly. The existing techniques used by various authors for peripheral blood cell classification is presented in Section 2. The formulation of the DSSCS algorithm is presented in Section 3. The different concepts and techniques used in the proposed methodology is provided in Section 4. The extensive experiments conducted to evaluate the efficiency of the proposed model using the hospital dataset is demonstrated in Section 5. The paper is concluded with a brief summary in Section 6. 2 Review of related works Over the past decade, many researchers have used various techniques to identify and classify peripheral blood cells. In [18, 19], the authors used SVM, K-PCA, and CNN for the classification of WBCs, namely basophil (BPHL), eosinophil (EPHL), monocytes (MCTY), lymphocytes (LCTY), neutrophil (NPHL) in the real-time data set and it achieved the accuracy of 97.6% also when the author used ALL_DB1, ALL_DB2, Dataset3 and it achieved accuracy 92.9%. However, the increasing size of the data set reflects in the accuracy of the results which is considered as a major drawback. In [3], the WBC classification of leukaemia was done by SVM, and the peripheral blood cells were classified using NN. They used the data set from KMC hospital, Manipal, and achieved an accuracy of 95.7%. This method only takes into account the five types of peripheral blood cells for classification and leaves out the important details that detect other abnormalities. Elsalamony [11] uses a NN approach to identify anaemia from the RBCs. They used a circular Hough transform and morphological tools to enhance the NN processing and clearly distinguish three types of anaemic blood cells. However, this technique is not applied to classify other diseases rather than anaemia. Jha and Dutta [10] applied a Chrono-SCA-ACNN approach for the detection of leukaemia [10] from the images obtained by the ALL_IDB2 database. The actor–critic NN (ACNN) was used to classify the various types of leucocytes from the LISC data set and the authors used only five types of classifications and obtained an accuracy of 98.3%. In [9, 20], the authors used the SVM classifier to classify the peripheral blood cells, such as BPHL, EPHL, MCTY, LCTY, and NPHL. By evaluating the method in real-world data sets, an accuracy of 90% is obtained from the experimental findings. The complexity of the classification process increases when this method is applied to a larger data set with varying records. The region-based CNN (R-CNN) [21] is applied to identify the WBC types in blood images obtained from the MIT licensed blood cell count and detection data set. However, this method is not suitable for real-time processing. The RosetteSep Applied Imaging Rare Event (RARE) approach [22] employed for the tumour cell quantification used the samples from carcinoma patients and achieved the best classification rates. To detect and classify the leukaemia cells, such as myeloblast (MBST), myelocyte (MCY), and segmented cell (SGC), the artificial NN (ANN) and decision trees are used and also the authors used a real-time data set from the hospital for classification which yielded a maximum accuracy. However, they considered only three types of leukaemia cells. [23]. Table 1 shows the literature review on peripheral blood cells. Table 1. Literature review on peripheral blood cells The author, year Purpose Classifiers Classified objects Database Accuracy, % Habibzadeh et al. [24] automatically classify WBC cells in low-resolution cytological images SVM, K-PCA, CNN BPHL, EPHL, MCTY, LCTY, and NPHL real-time data set 97.6 Shahin et al. [18] detection method for WBC based on deep CNN CNN BPHL, EPHL, MCTY, LCTY, and NPHL ALL_DB1, ALL_DB2, Dataset3 92.9 Hegde et al. [3] detection of leukaemia, WBC classification process SVM and NN BPHL, EPHL, MCTY, LCTY, and NPHL data set from KMC hospital, Manipal 95.7 Elsalamony [11] detection and counting of microscopic image employing Hough transform and morphological tools NNs sickle, microsites, elliptocytosis, unknown shaped cells. real-time data set 97.1 Jha and Dutta [10] automatic recognition of leukaemia Chrono-SCA-ACNN approach BPHL, EPHL, MCTY, LCTY, and NPHL ALL_IDB2 database 98.3 Gupta et al. [19] classification of various types of leucocytes optimised binary bat algorithm BPHL, EPHL, MCTY, LCTY, and NPHL LISC data set 97.3 Zheng et al. [9] enhance the accuracy as well as adaptability of WBC segmentation SVM BPHL, EPHL, MCTY, LCTY, and NPHL from Zhongnan Hospital and Wuhan landing Medical high Tech Co. Ltd. 96.5 Wang et al. [20] enhance accuracy to reduce complexity SVM BPHL, EPHL, MCTY, LCTY, and NPHL real-time data set 91.2 Kutlu et al. [21] easily detect WBC types in blood images R-CNN BPHL, EPHL, MCTY, LCTY, and NPHL MIT licensed BCCD data set 98.5 Naume et al. [22] easily quantify the isolated tumour cells RARE approach erythrocytes and leucocytes samples from carcinoma patients 88.5 Negm et al. [23] detect the leukaemia cells ANN, decision trees MBST, MCY, and SGC data set from the local hospital 98.5 Khouani et al. [25] proposed a deep learning approach to automatically identify the WBCs from the bone marrow and peripheral blood cell images obtained. Here, the input is preprocessed before sending it to the deep NN. The deep NN is responsible for cell localisation and image segmentation. The segmentation quality is enhanced using a novel algorithm that utilises the relationship between the model outcomes and spatial information obtained. However, this method is not capable of identifying different types of blood cells. Yang et al. [26] presented a deep learning approach for smartphones to identify the malaria parasites from thick blood smears. This model includes two processing steps. In the first step, an intensity-based iterative global minimum screening (IGMS) is implemented to undergo a rapid screening of the parasite candidates in the blood smear images. In the next step, the CNN classifies each candidate observed as a parasite or background. However, this model suffers from a higher running time complexity. Kumar et al. [27] developed a deep learning approach using CNN to automatically identify the white blood cancer from the bone marrow microscopic images obtained. In this work, two different types of cancers are identified namely acute lymphoblastic leukaemia (ALL) and multiple myeloma (MM). Nevertheless, this work entails certain limitations and one of them is that they did not consider the data set size to conduct extensive experiments. Shemona and Kumar [28] presented a deep learning classifier to identify the blood cancer at an early stage from the RBC utilising an online region-based segmentation (ORBS) technique. The ORBS technique is used to identify the corpuscles region in the blood. Jha and Dutta [29] proposed a hybrid model utilising mutual information and deep learning to detect ALL from single-cell blood smear images. The mutual information for segmentation is attained from integrating the details from the fuzzy C-means algorithm and the active contour model. A chronological sine cosine algorithm is embedded with the deep CNN classifier to classify the segmented images after feature extraction. 3 Formulation of DSSCS algorithm The DSSCS algorithm is formed by the combination of the SSA and CSO, and these two are combined by using the disruption operator. The SSA and CSO are hybridised to enhance the capability of exploration of SSA. CSO algorithm: Based on the number of cats present in the swarm, the global best solution is achieved in the CSO algorithm. Based on the target of the existing problem, the solution for each cat is determined depending on their location. The CSO algorithm has two modes: tracing and seeking [30]. The seeking mode represents the cat is at rest position and getting ready to shift to the next location and this movement is very slow. The tracing mode shows the cat tracks the targets by quickly changing the locations. Tracing mode: When the prey is hunted, the tracing mode creates a cat. After the prey is located in the seeking mode, the cat determines its movement speed and the direction depends on the prey's position and speed (velocity). The velocity of the cat l is updated in dimension is given by V l = w V l + q × b ( A best − A l )(1) where V l represents the velocity of cat l; Abest is the cat position with the best solution. Al location of the cat l; b is constant, w is the inertia weighted parameter, and q is the random value in the interval [0, 1]. The location of the cat is updated according to the following equation: A l , new = A l , old + V l (2) where Al,old represents the present location of cat l and Al,new represents the new location of the cat l. The value of the position Al can be updated using (2). Here, Al,new represents the updated position, and Al,old represents the actual position of the cat. 3.1 Salp swarm algorithm SSA is developed by Mirjalili et al. [31]. The main scheme behind the SSA operators is that a swarm called the salp chain sometimes forms a swarm to reproduce the swarming action of salps in the seas. The leader's function is to guide the salp swarm; every follower follows the former one. Similar to other algorithms, SSA begins with the process by initialising the random salp population, then calculates the fitness function for every salp. The best fitness value of salp is indicated as the leader salp, whereas the other salps are followers. The most excellent location between all salp is allocated by the notation B as the food source to be hunted by the chain of the salp. The salp chain location is updated by the two phases: leader phase and follower phase. The salp leaders position is updated using the following equation: A i 1 = B i + b 1 ( ( u b i − l b i ) b 2 + l b i ) b 3 ≥ 0.5 B i − b 1 ( ( u b i − l b i ) b 2 + l b i ) b 3 < 0.5 (3) where A i 1 represents the salp leader position, Bi represents the source of food in the ith dimension, l b i and u b i are the lower and upper bounds of the ith dimension. The randomly created numbers are represented as b2 and b3 in the interval [0, 1], and the b1 represents the significant aspect in SSA, and it is reliable for the balance among the exploitation and exploration, and it can be described as b 1 = e − ( 4 v / V ) 2 (4) where v denotes the present iteration, as well as V, is the maximum number of iterations. The follower phase position is updated using the following equation: A i k = 1 2 ( A i k + A i k − 1 ) (5) where k ≥ 2 and A i k is the location of the kth salp follower at the ith dimensions. The SSA pseudo-code is depicted in Algorithm 1 (see Fig. 1). Fig. 1Open in figure viewerPowerPoint Algorithm 1: SSA 3.2 Disruption operator The disruptive operator (Do) [32] is intended to boost the diversification capability for the entire SSA or CSO population. The disruption operator is evaluated to attain the objective is calculated as shown as D o = D k , i × χ ( − 2 , 2 ) if D k , best ≥ 1 1 + D k , best × χ ( − 10 − 4 2 , 10 − 4 2 elsewhere (6) The Euclidean distance among the kth solution and its closest neighbourhood ith solution is Dk,i, whereas the Euclidean distance among the kth solution with the best solution is Dk,best. The χ(c, d) is the random number that is created within the range [c, d]. 3.3 DSSCS optimisation algorithm The structure of the DSSCS optimisation algorithm is shown in Fig. 2 and Algorithm 2 (see Fig. 3). This depends upon improving the SSA behaviour by employing the operators of the CSO as well as the Do operator. The objective of the CSO is to increase the exploration capability of the followers to explore marginally more than the conventional SSA's mechanism update. The Do is to increase the diversification of the whole population once after the update by utilising the SSA or CSO operators that leads to enhance exploration capability of DSSCS and convergence to the most favourable solution. The proposed DSSCS algorithm structure is shown in Fig. 4. There are three stages in the proposed structure; they are initialisation stage, update stage, and an ending stage. Fig. 2Open in figure viewerPowerPoint Process of the proposed system (a) Overall structure of the proposed system, (b) Schematic illustration of settings of the parameter in position array, (c) Classification of peripheral blood cells Fig. 3Open in figure viewerPowerPoint Algorithm 2: Pseudocode for DSSCS algorithm Fig. 4Open in figure viewerPowerPoint Structure of the DSSCS algorithm The input parameters are set during the initialisation process, the population is initialised, the numerical parameter value is assigned and the numerical parameter is converted into a binary solution, and then the objective function for each layer of CNN is evaluated. By calculating the objective function, the evaluation stage begins with the assessment of each solution quality. The second phase is the position update stage; after the objective function is evaluated in the initial stage, the best solution is selected. Next, the whole population is classified into the leader phase and follower phase. The leader phase is updated using SSA whereas the follower phase is updated using CSO. The disruption operator employs to combine the updated leader phase and the updated follower phase. The objective of the disruption operator is to improve the population variety and to sustain the balance among searching as well as utilisation processes. In the ending stage, the condition for the stopping criterion is checked. Whether the condition is satisfied it gets back the best solution if the stopping criterion is not satisfied to obtain another solution to find the objective function. 4 Proposed approach The key point of this system is to develop a model for the categorisation of the peripheral blood cells utilising CNNs [33]. The peripheral blood cells are classified into eight classes such as neutrophils, basophils, eosinophils, monocytes, immature granulocytes, lymphocytes, platelets, and erythroblasts. The proposed model takes the blood image as input and outputs the corresponding class it belongs to. The DSSCS optimization technique is used to fine-tune the CNN and which is then retrained using the input peripheral blood cell image dataset to yield results with high classification accuracy. The overall structure of the system for the recognition of the blood cells is shown in Fig. 2a. 4.1 Basic CNN architecture CNN [34] is mainly used for feature extraction and classification and it is widely deployed in several challenging tasks such as object/action recognition, image classification, etc. The CNN comprise of a convolution feature extraction block associated with a fully connected block. The convolutional feature extraction block comprises of a sequence of convolution and pooling layers. CNN mainly implements two additional layers (pooling and convolution) in the place of a fully-connected layer to reduce parameter size. At first, the raw pixels are taken as input and the result is a class of which the representation is derived from the input probability value. The filter (stride) and kernel size are fixed beforehand. By applying appropriate filters, CNN is capable of capturing both the temporal and spatial dependencies in the image. The interconnected neurons in CNN have their own weight, biases, and activation function. This work utilises a CNN, which is built by repeatedly interconnected five layers, namely convolution, activation, pooling, fully connected, and classification. The feature extraction is mainly done by the convolutional layer by convolving the input features. The volume structure of the convolutional layer comprises of P number of planes and each plane has a set of kernels (filters) of size F. For example, if the value of F is taken as three means it is a filter with a 3 × 3 matrix with its associated weights. The weight of a filter present in the yth plane is transformed into a matrix ω n y and the value matrix of the nth input plane is taken as An. This process yields a two-dimensional matrix ω n y ∗ A n when the convolutional filter slides on the nth input plane. For every input planes present in the convolutional layer, these matrices formed up are added along with a bias matrix βn, and the outcome is transferred into an activation function shown in the equation below: M n = f β n + ∑ n = 1 D ω n y + A n , y = 1 , 2 , … , N(7) The output matrix Mn is the feature map obtained. The non-linear activation layer is present next to the convolutional layer. The different activation functions are non-saturating the rectified linear unit (ReLU), saturating hyperbolic tangent, or the sigmoid function. Equations (8)–(10) present ReLU, tangent, and sigmoid functions. f ( a ) = max ( 0 , a )(8) f ( a ) = tanh ⁡ ( a ) = | tanh ⁡ ( a ) |(9) f ( a ) = ( 1 + e − a ) − 1 (10) The activation function is mainly used to enhance the classification and learning capabilities of the network. ReLU primarily brings non-linearity to the CNN, since real-world input is a linear value that is primarily non-negative. Since the performance of ReLU is higher than other activation functions, in this work it is used. The main objective of the pooling layer is to perform a non-linear down-sampling operation to reduce the spatial size. The pooling layer is capable of simultaneously reducing the computational complexity, a number of parameters, and overfitting issues in the network. The maximum value obtained when the kernel analyses a portion of an image is known as max pooling. The average value returned by the kernel when analysing a portion of an image is known as average pooling. The two pooling layers can be statistically explained using the below formulas: MP = R max m , m I (11) AP = R avg m , m I (12) Here R max m , m I and R avg m , m I represents the maximum and average pooling functions, respectively, where I is the input vector, m, m is the block size, MP is max-pooling output, and AP is average pooling output. This work adopts max-pooling because of its capability to act as a noise suppressant. Max pooling is mainly utilised in this work to act as a noise suppressant which simultaneously removes the noise from the activation function and performs dimensionality reduction. The performance offered by max-pooling is higher than average pooling. The neurons present in the fully-connected layer are fully connected to the previous layer activations. These neurons undergo the input–output operation as shown in the equation below: O = f B + ∑ j n w j a j (13) Here the input feature is represented as aj, the weight of the neurons is represented as wj, B is the bias value, f(.) is the activation function, and n is the total number of features present in the dataset. The output O obtained using equation (13) is given as the input to the fully connected layer. The non-linear combinations of the high-level features are learned using the fully-connected layer which provides the output of CNN. The fully connected configuration is also provided for the classification layer and it uses the softmax as an activation function. The functioning of the softmax layer is depicted as shown below: f ( m y ) = softmax ( m y ) = e ( m y ) ∑ n = 1 8 e ( m y ) , where y = 1 , 2 , … , 8 (14) The probability distribution value of each class is derived using the softmax activation function. Since the proposed work classifies eight different peripheral blood cells, eight neurons have been set up in the above equation. 4.2 Framework of DSSCS–CNN approach The hyperparameter problem in CNN is solved using the DSSCS algorithm by the following procedure. To select an appropriate distribution arrangement as well as the appropriate number of network layers, it is more significant to control the number of hyperparameters as well as the allocation of the network layers. Particularly the input is a parameter that converts to the binary arrangement of 1 and 0 s. There are two layers on CNN; they are the convolutional layer and the pooling layer. Hence the convolutional layer is treated as binary 1, whereas the pooling layer is treated as binary 0, respectively. The numerical input parameter 7897, next a 13-bit binary number (1111011011001) is achieved; thus, the created CNN approach consists of 9 convolutional layers and 4 p