Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images

Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conclusion. Pancreatic cancer is a lethal type of cancer, and its prediction is ineffective with current techniques. Automated detection and classification of pancreatic tumors can be provided by the computer-aided diagnosis (CAD) model using radiological images such as computed tomography (CT) and magnetic resonance imaging (MRI). The recently developed machine learning (ML) and deep learning (DL) models can be utilized for the automated and timely detection of pancreatic cancer. In light of this, this article introduces an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images. The major intention of the IDLDMS-PTC technique is to examine the CT images for the existence of pancreatic tumors. The IDLDMS-PTC model derives an emperor penguin optimizer (EPO) with multilevel thresholding (EPO-MLT) technique for pancreatic tumor segmentation. Additionally, the MobileNet model is applied as a feature extractor with optimal auto encoder (AE) for pancreatic tumor classification. In order to optimally adjust the weight and bias values of the AE technique, the multileader optimization (MLO) technique is utilized. The design of the EPO algorithm for optimal threshold selection and the MLO algorithm for parameter tuning shows the novelty. A wide range of simulations was executed on benchmark datasets, and the outcomes reported the promising performance of the IDLDMS-PTC model on the existing methods.


Introduction
Pancreatic cancer is relatively rare, but it can be a leading cause of death [1,2]. Recently, the survival rate of pancreatic cancer has been low, and its 5-year survival rate gets drastically reduced to 11% [3]. The surgical resection of the primary tumor is possible; in less than 20% of patients, 5-year survival increased to 20-37% [4]. The evidence that

The Proposed Model
In this study, a novel IDLDMS-PTC technique was derived from examining the CT images for the existence of pancreatic tumors. The proposed IDLDMS-PTC technique comprises several subprocesses, namely GF-based pre-processing, EPO-MLT-based segmentation, MobileNet-based feature extraction, AE-based classification, and MLO-based parameter optimization. The utilization of the EPO approach to better threshold selection and the MLO algorithm for parameter tuning assists in accomplishing improved classification results. Figure 1 illustrates the overall process of the IDLDMS-PTC technique.
Healthcare 2022, 10, x 3 of 19 Asadpour et al. [14] presented a cascaded architecture for extracting the tumor in adenocarcinoma patients and the volumetric shape of the pancreas. This method is an integration of an elastic atlas that is able to fit on three-dimensional volumetric shapes extracted from CT slices, a CNN using three forwarded paths to label the patches of the image with coarse to fine resolution with a multi-resolution architecture.
The capacity of DL is estimated in [15] to distinguish pancreatic disease on contrastenhanced magnetic resonance (MR) images with the help of a generative adversarial network (GAN). Classification accuracy of trained InceptionV4 architecture for each patient and patch were made on the validation set, correspondingly. Iwasa et al. [16] estimated the ability of DL for the automated segmentation of pancreatic tumors on CE-EUS video images and the probable factor that affects the automated segmentation. Automated segmentation was implemented by U-Net with 100 epochs and estimated by using four-fold cross-validation. The tumor boundary (TB) and degree of respiratory movement (RM) were classified as to 3-degree intervals from the patient and estimated as feasible factor that affects the segmentation.

The Proposed Model
In this study, a novel IDLDMS-PTC technique was derived from examining the CT images for the existence of pancreatic tumors. The proposed IDLDMS-PTC technique comprises several subprocesses, namely GF-based pre-processing, EPO-MLT-based segmentation, MobileNet-based feature extraction, AE-based classification, and MLO-based parameter optimization. The utilization of the EPO approach to better threshold selection and the MLO algorithm for parameter tuning assists in accomplishing improved classification results. Figure 1 illustrates the overall process of the IDLDMS-PTC technique.

Gabor Filtering Based Pre-Processing
At the initial stage, the pre-processing of the CT images is performed by the use of the GT technique. It is a linear filter whose impulse response is a sinusoidal function multiplied by a Gaussian function. They are nearly passband function. The major benefit of

Gabor Filtering Based Pre-Processing
At the initial stage, the pre-processing of the CT images is performed by the use of the GT technique. It is a linear filter whose impulse response is a sinusoidal function multiplied by a Gaussian function. They are nearly passband function. The major benefit of presenting the Gaussian envelope is that the Gabor function is situated in spatial and frequency domains, different from the sinusoidal function, which is entirely delocalized in the spatial (sinusoidal function covers the whole space) and localized accurately in the frequency domain [17]. Thus, this function is highly suited for representing a signal in these domains. Gabor is a 2D bandpass filter; when allocated a frequency and direction, the address of the original image is conserved, and noise is reduced.

EPO-MLT-Based Segmentation
In the image segmentation process, the EPO-MLT approach was developed for determining the tumor regions in the CT images. The MLT issue was demonstrated by assuming a gray level image I that was segmented containing K + 1 classes [18]. Therefore, K thresholds {t 1 , t 2 , . . . , t K } are needed for dividing the image into sub-regions as in Equation (3): where C k implies the k th class of images, t k (k = 1, . . . , K) refers to the k th threshold values, g(u, v) stands for the gray level of pixels (u, v), and L signifies the gray level of I; these levels are from the range (0, 1 . . . L − 1). The vital drive of multilevel thresholding is for locating the threshold value that divides pixels into many groups, which are defined as maximized in the subsequent formula: where F(t 1 , . . . , t K ) refers to the Otsu's function, which is determined as: where η 1 refers to the mean intensity of I with t 0 = 0 and t K+1 = L. The h(u) and P u implies the frequency and probability of u th gray level; correspondingly, N p stands for the entire number of pixels from I. In order to define the optimum threshold values for the MLT approach, the EPO algorithm is applied. The EPO was stimulated by the emperor penguins (EPs) huddling attitudes as initiated in the Antarctic [19]. In order to forage, the EPs usually travel from rafl/colonies. Therefore, an initial function is for defining an effective mover in the swarm from the mathematical progress. The distances amongst Eps X ep were calculated for achieving this, then its temperature profile (θ ). The temperature profile of EPs is measured as: The higher number of iterations, where C implies the existing iteration, was stated as Iter max , and R implies the arbitrary number between zero and one.
As EPs usually huddle together to preserve temperature, careful precaution is taken to protect them from neighborhood collisions. Consequently, it presents two vectors ( → U) and ( → V) whose values are estimated as: where M indicates movement with the fixed value of 2, Equations (13) and (14) are created for calculating approximately the distance among EP and optimally fittest searching agents ( → D). S() illustrates the social force to that an optimally searching agent was managed by EPs; e represents the exponential functions.
At this point, based on the optimum agents achieved utilizing Equation (15), the places of EPs were upgraded.
It must be apparent that the parameter range selected is equal to individuals of new works. Therefore, the EPO technique has been employed for obtaining the optimal global value with respect to the operator. In EPO, with arbitrarily created individual EPs, the population of emperor penguins is initialized.

MobileNet-Based Feature Extraction
At the time of feature extraction, the segmented images are passed into the MobileNet model to generate feature vectors. AI has connected the gap between the abilities of machines and humans. Computer vision is a field of AI that allows machines to observe the world like humans. The advancement in this field has been completed over one certain approach named a CNN. CNN contains input, hidden, and output layers [20]. To design a diagnostic system for pancreatic tumor classification, in this study, the MobileNet model is utilized for feature extraction. MobileNet is faster when compared to convolution networks because of its different filter methods for every response channel. This method is built on depth-wise separable convolution that has two successive functions: one is a depth-wise convolutional at the filter phase, which employs convolutional to a single input channel at a time, and the other is a point-wise convolution at the filtering phase, which implements linear integration of output to the depth-wise convolution. ReLU and Batch normalization (BN) layers come after the convolutional process. Computation cost phenomenally reduces in the depth-wise separable model because of filtration at the integrating phase to minimalize its complexity and size. The version applied here employs MobileNet V2 with 3.47 million parameters.

Optimal AE-Based Classification
Finally, the AE model is used to detect and classify the presence of pancreatic cancer. An AE employs a set of weights recognition for encoding an input vector x into a depiction vector h, represented as latent parameters [21]. Then, it employs a set of weights generative for decoding the depiction vector into an estimated reconstruction of the input vector x . The aim of the AE is to recreate the input information in an unsupervised manner, that is, without utilizing any labels when the dimensionality of the input and the output need to be identical.
The encoder phase of AE takes x ∈ R m as input and maps to latent parameter h ∈ R n : where f denotes an activation function, namely sigmoid, s(x) = 1/1 + e −x or ReLU, W indicates a weight matrix, and b represents a bias vector. Then, the decoder phase maps h to x that is a reconstruction of x with similar dimensionality.
Here, f , W , and b indicate the respective parameters for the decoder that may be distinct from the encoder one. AE is trained for minimizing reconstruction errors, including mean squared error (MSE): x is usually averaged through n trained instances. For determining the weight and bias values of the AE technique, the MLO approach can be utilized. It is arithmetically modeled for implementing optimization problems. The major concept of the presented approach is to utilize data from the members of the population. In such cases, member of the population uses the data of different leaders for searching in the problem-solving space as follows: where X i represent the ith member of the population, and χ d i indicates the dth parameter of optimization problem. Members of the population are estimated by placing them in the fitness function [22]. Next, the population matrix is arranged according to the minimum values of the fitness function as follows: Here, X sort indicates the matrix of population, X sort 1 represent the member with optimal fitness value, X sort N indicates the member with worst fitness value, X r 1 shows the member of the population with initial rank-based fitness value, X r N represent the member of the population with worst rank-based fitness value, fir indicates the fitness value, and N signifies the number of members of the population. After arranging the population matrix, some amount of the ranked population is chosen as the leader. The leader is upgraded with all the iterations to guide members of the population towards the optimum solution as follows: where L indicates the selected leader member matrix and n L shows the leader's number. The population in MLO is upgraded as follows. Initially, all the members of the population are moved in the searching space according to the leader position. The leader is defined In the equation, f it n i indicates the normalized fitness function for the ith population member, P l represent the possibility of selection of l'th leader for guiding the parameter, C l indicate the cumulative probability of l'th leader, x d i,new shows the new value for dth dimension of ith parameter, L d i,c denotes the dth dimension of selected cth leader for guiding dth parameter of ith population member, and r shows the arbitrary value within [0, 1]. Next, after upgrading the initial stage, all the members of the population make a slight random move in their own neighborhood. When the new location is highly relevant, the member upgrades its location to new status or else returns to its preceding location.
Here, t denotes the t th iteration, and T represent the maximal number of iterations. The MLO algorithm has determined a fitness function of tuning the parameter values of the AE model. The fitness function is used to determine a positive integer for the representation of effectual outcomes of the candidate solutions. Here, the objective is to minimize the classification error rate as given below.

Experimental Validation
This section investigates the pancreatic tumor detection and classification performance of the IDLDMS-PTC model using test CT images collected from various sources. The dataset holds a total of 500 images, with 250 images under pancreatic tumor and 250 images under non-pancreatic tumor. The results are investigated under varying sizes of training/testing datasets. Figure 2 depicts sample CT images.   The accuracy of the IDLDMS-PTC approach under training set (80:20) data is portrayed in Figure 4. The results demonstrated that the IDLDMS-PTC technique accomplished improved validation accuracy compared to training accuracy. It can also be observed that the accuracy values get saturated with the count of epochs.     The accuracy of the IDLDMS-PTC approach under training set (80:20) data is portrayed in Figure 4. The results demonstrated that the IDLDMS-PTC technique accomplished improved validation accuracy compared to training accuracy. It can also be observed that the accuracy values get saturated with the count of epochs. The loss outcome analysis of the IDLDMS-PTC technique under training set (80:20) data is depicted in Figure 5. The figure exposed that the IDLDMS-PTC technique has denoted the reduced validation loss over the training loss. It can be additionally observed that the loss values get saturated with the count of epochs. The loss outcome analysis of the IDLDMS-PTC technique under training set (80:20) data is depicted in Figure 5. The figure exposed that the IDLDMS-PTC technique has denoted the reduced validation loss over the training loss. It can be additionally observed that the loss values get saturated with the count of epochs.  The accuracy outcome analysis of the IDLDMS-PTC technique under CV of 7 data is illustrated in Figure 7. The results demonstrate that the IDLDMS-PTC technique accomplished improved validation accuracy compared to training accuracy. It is also observable that the accuracy values get saturated with the epoch count. The loss outcome analysis of the IDLDMS-PTC technique under CV of 7 data is displayed in Figure 8. The figure demonstrates that the IDLDMS-PTC methodology signified the reduced validation loss over the training loss. It is additionally noted that the loss values get saturated with the epoch count.    The accuracy outcome analysis of the IDLDMS-PTC technique under CV of 7 data is illustrated in Figure 7. The results demonstrate that the IDLDMS-PTC technique accomplished improved validation accuracy compared to training accuracy. It is also observable that the accuracy values get saturated with the epoch count.  The loss outcome analysis of the IDLDMS-PTC technique under CV of 7 data is displayed in Figure 8. The figure demonstrates that the IDLDMS-PTC methodology signified the reduced validation loss over the training loss. It is additionally noted that the loss values get saturated with the epoch count.

Discussion
In order to further ensure the betterment of the proposed model, a detailed comparative study of the IDLDMS-PTC approach with recent approaches is offered in Table 3 [23][24][25][26].

Discussion
In order to further ensure the betterment of the proposed model, a detailed comparative study of the IDLDMS-PTC approach with recent approaches is offered in Table 3 [23][24][25][26].         Figure 11 compares the acc y examination of the IDLDMS-PTC system with existing algorithms. The figure revealed that the CNN-10x10 and CNN-30x30 methods obtained lower acc y values of 0.8160 and 0.8590, respectively. Similarly, the CNN-50x50 and CNN-70x70 methods attained slightly increased acc y values of 0.8730 and 0.8740, respectively. Additionally, the WELM, KELM, and ELM algorithms resulted in moderately closer acc y of 0.9726, 0.9669, and 0.9621, respectively. Eventually, the ODL-PTNTC system has accomplished near-optimal acc y of 0.9840, and the presented IDLDMS-PTC technique has reached a maximum acc y of 0.9935. By looking into the above-mentioned tables and figures, it can be ensured that the IDLDMS-PTC model resulted in superior pancreatic tumor detection and classification performance over the other methods. The reduced network size, minimum parameters, and faster performance of the MobileNet model help in assisting improved performance. Additionally, the utilization of the EPO technique for optimum threshold selection and the MLO algorithm for parameter tuning assists in accomplishing improved classification results.

Conclusions
In this study, a novel IDLDMS-PTC approach was derived from examining the CT images for the existence of pancreatic tumors. The proposed IDLDMS-PTC technique comprises several subprocesses, namely GF-based pre-processing, EPO-MLT-based segmentation, MobileNet-based feature extraction, AE-based classification, and MLO-based parameter optimization. The utilization of the EPO technique to optimum threshold selection and the MLO algorithm for parameter tuning assists in accomplishing improved classification results. To assess the effectiveness of the IDLDMS-PTC technique, a comprehensive experimental analysis was carried out on a benchmark dataset. Extensive comparative outcomes exposed the promising performance of the IDLDMS-PTC model over the existing methods. Therefore, the IDLDMS-PTC technique can be utilized as an effective tool for the healthcare system. In the future, deep instance segmentation approaches will be applied to improve the classifier results of the IDLDMS-PTC model. By looking into the above-mentioned tables and figures, it can be ensured that the IDLDMS-PTC model resulted in superior pancreatic tumor detection and classification performance over the other methods. The reduced network size, minimum parameters, and faster performance of the MobileNet model help in assisting improved performance. Additionally, the utilization of the EPO technique for optimum threshold selection and the MLO algorithm for parameter tuning assists in accomplishing improved classification results.

Conclusions
In this study, a novel IDLDMS-PTC approach was derived from examining the CT images for the existence of pancreatic tumors. The proposed IDLDMS-PTC technique comprises several subprocesses, namely GF-based pre-processing, EPO-MLT-based segmentation, MobileNet-based feature extraction, AE-based classification, and MLO-based parameter optimization. The utilization of the EPO technique to optimum threshold selection and the MLO algorithm for parameter tuning assists in accomplishing improved classification results. To assess the effectiveness of the IDLDMS-PTC technique, a comprehensive experimental analysis was carried out on a benchmark dataset. Extensive comparative outcomes exposed the promising performance of the IDLDMS-PTC model over the existing methods. Therefore, the IDLDMS-PTC technique can be utilized as an effective tool for the healthcare system. In the future, deep instance segmentation approaches will be applied to improve the classifier results of the IDLDMS-PTC model. Data Availability Statement: Data sharing is not applicable to this article, as no datasets were generated during the current study.