The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe. Radiologists use X-Rays or Computed Tomography (CT) images to confirm the presence of COVID-19. So, image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times. The current research work introduces Multi-objective Black Widow Optimization (MBWO)-based Convolutional Neural Network
The outbreak of a novel coronavirus (nCoV) (officially named as ‘SARS-CoV2’) was first identified by the researchers in Wuhan, China by December 2019. Later, this virus was found to be a causative agent of COVID-19, a contagious life-threatening disease. Though its outbreak was first reported in China, it quickly travelled to global nations from January 2020 itself [
At few instances, virus-affected individuals produce negative results which mostly ends up in increased fatality rate. Virus-affected individuals spread the virus unknowingly to other people who are healthy and normal, since COVID-19 is an extremely communicable disease. The medical reports of infected people especially chest Computed Tomography (CT) scan images confirm the occurrence of bilateral modification. Thus, chest CT, a highly sensitive technique, is applied as a secondary confirmation in disease prediction and diagnosis of SARS-CoV2. It becomes mandatory for a radiologist to examine these chest CT images obtained from infected individuals. In this scenario, the development of a Deep Learning (DL)-based detection technique becomes inevitable to examine the chest CT scan images without the help of a radiologist.
Artificial Intelligence (AI) is one of the advanced technologies that has been extensively applied in the acceleration of biomedical applications. Under the application of DL frameworks, AI is employed in several domains such as image analysis, data categorization and image segregation and so on. Individuals, infected with COVID-19, suffers from pneumonia since the virus first attacks the respiratory tract and then enters the lungs gradually. Many DL works have been conducted so far with the help of chest X-ray image data model. Previously, a number of studies leveraged pneumonia X-ray images using three diverse DL methodologies like fine-tuned technique, technique with no fine-tuning, and technique trained from scrap. With respect to ResNet approach, the dataset is first classified under several labels such as age, gender, etc., Then, Multi-Layer Perceptron (MLP) is applied as a classifier since it produces the highest accuracy.
A classification model was proposed in the literature [
A DL approach was introduced in the study conducted earlier [
In recent times, developers have presented an imaging pattern of chest CT scan to predict COVID-19 [
Shan et al. [
Narin et al. [
The current research article proposes a new COVID-19 diagnosis and classification model using multi-objective Black Widow Optimization-based Convolutional Neural Network (MBWO-CNN). The proposed MBWO-CNN model detects and classifies COVID-19 through image processing technique. The technique has four steps such as preprocessing, feature extraction, parameter tuning, and classification. Initially, the input images undergo preprocessing followed by CNN-based feature extraction. Then, multi-objective Black Widow Optimization (MBWO) algorithm is implemented to tune the hyperparameters of CNN. The application of MBWO algorithm helps in improving the performance of CNN. Finally, Extreme Learning Machine with Autoencoder (ELM-AE) is applied as a classifier to detect the presence of COVID-19 and categorize it under different class labels. The proposed MBWO-CNN model was experimentally validated in this study and the results obtained were compared with existing techniques.
Once the input image is preprocessed, feature extraction is carried out using MBWO-CNN model. The application of MBWO algorithm helps in the selection of initial hyperparameters of CNN.
CNN is a subfield of DL model which implies the maximum breakthrough from image analysis. CNN is predominantly applied in the examination of visual images during image classification process. Both hierarchical infrastructure as well as effective feature extraction of an image makes CNN, the most preferred and dynamic approach for image categorization. Initially, the layers are arranged in 3D format.
The neurons in the applied layer are not designated as the complete collection of neurons in the secondary layer. In other words, a minimum number of neurons is available in the secondary layer too. Consequently, the result gets degraded as a single vector of possible values, which are incorporated together in the dimension of depth.
CNNs are composed of input, output, and hidden layers. Usually, the hidden layers are comprised of convolution, ReLU, pooling, as well as Fully Connected (FC) layers.
Convolution layer uses convolution task for input. It sends the data to consecutive layer Pooling layer concatenates the results of clusters with a neuron present in subsequent layer FC layers link all the neurons as a single layer, with other neurons present in the subsequent layer. In case of FC layer, the neurons obtain the input from all the elements of existing layer
CNN functions on the basis of obtaining features from the images. There is no need to perform manual feature extraction. Hence, the features remain unequipped and it gains knowledge at the time of network training with a collection of images. Training process makes the DL model highly effective in computer vision operations. CNNs perform feature prediction with the help of massive number of hidden layers. The layer enhances the difficulty of learned features [
According to the literature, CNN gains experience from hyperparameter tuning problems. The hyperparameters are kernel size, padding, kernel type, hidden layer, stride, activation functions, learning value, momentum, epoch count, and batch size. These variables should be tuned. Here, multi-objective Fitness Function (FF) is expressed as follows.
where,
Here,
Here,
(i) Initial Population
Optimization issue can be resolved using the measures of problem scores, which develop a proper architecture for the solution developed from a recent problem. In GA and PSO methodologies, the structure is termed as ‘Chromosome’ and ‘Particle position’ correspondingly. However, in BWO, it is termed as ‘widow’. At this point, a capable solution is assumed for all the problems in the form of a black widow spider. These black widow spiders depict the measures of problem variables. Further, to resolve the standard functions, the infrastructure is demonstrated as an array.
For
The variable measures
In order to invoke the optimization method, a candidate widow matrix of size
(ii) Procreate
As pairs are autonomous in nature, it begins to mate and gives birth to next generation. Mating process occurs in the web. An approximate of 1,000 eggs is laid during every mating. Some of the spiderlings die due to different reasons, while the healthy one stays alive. For reproduction, an array named ‘alpha’ is developed. This is to ensure that the widow array is generated using arbitrary values of the offsprings, under the application of
It is followed for (iii) Cannibalism
There are three types of cannibalism reported so far. Sexual cannibalism, where a female BW consumes the male after mating. Here, both female and male are examined (iv) Mutation
In this process, the mutation value of the individuals are randomly selected to develop a population. The selected solutions are randomly interchanged in the array. It is estimated using a mutation rate.
(v) Convergence
Similar to EAs, the author assume three termination criteria such as predetermined values of iterations, observance of modifications in fitness measures of an optimal widow during different iterations and accomplishment of accuracy up to certain level.
(vi) Parameter Setting
The deployed BWO model is composed of few attributes such as Procreating Rate (PP), CR, and Mutation Rate (PM) to attain the best outcomes. The variable has to be modified to enhance the efficiency of this method and eventually, supreme solutions can be attained. At the time of fine tuning the parameters, the chances are high for moving from local optima to higher ability and finding the searching area globally. Therefore, the exact number of attributes assure that the management is effective between exploitation and exploration phases. The developed approach is composed of three significant controlling attributes such as PP, CR, and PM. Here, PP denotes the procreating ratio that calculates the number of individuals to be involved in procreation [
The current research article considered ELM-SA for classification. It applies the obtained features and estimates the possibility of objects present in an image. Both activation function as well as dropout layer are employed in the establishment of non-linearity and reduction of over-fitting issues correspondingly [
where
where
where
where
Followed by,
where
ELM is upgraded as Kernel-based ELM (KELM) through kernel trick. Suppose
Where
Here,
where
Assume
where
Besides,
where
This section discusses the classifier results achieved by MBWO-CNN model in the classification of chest X-Ray image [
On the other hand, when executing run 5, the MBWO-CNN model reported the maximum sensitivity of 95.47%, specificity of 95.80%, accuracy of 95.76%, and F-score of 96.10%. Likewise, under execution run 6, the MBWO-CNN model attained the maximum sensitivity of 97.32%, specificity of 96.43%, accuracy of 95.98% and F-score of 97.89%. The proposed MBWO-CNN model accomplished the maximum sensitivity of 95.32%, specificity of 96.21%, accuracy of 96.44%, and F-score of 96.55% under the execution of run 7. For execution of run 8, the MBWO-CNN model offered the maximum sensitivity of 96.32%, specificity of 95.93%, accuracy of 96.09%, and F-score of 97.30%. The maximum sensitivity of 95.78%, specificity of 96.90%, accuracy of 97.29% and F-score of 96.51% were achieved by the proposed model during the execution of run 9. Simultaneously, on the execution run of 10, the MBWO-CNN model yielded the maximum sensitivity of 95.84%, specificity of 95.66%, accuracy of 96.43%, and F-score of 97.10%.
No. of runs | Sensitivity | Specificity | Accuracy | F-score |
---|---|---|---|---|
Run 1 | 95.10 | 94.68 | 96.78 | 95.90 |
Run 2 | 95.16 | 97.12 | 96.22 | 95.65 |
Run 3 | 95.18 | 96.87 | 96.90 | 96.32 |
Run 4 | 96.20 | 95.90 | 96.43 | 97.45 |
Run 5 | 95.47 | 95.80 | 95.76 | 96.10 |
Run 6 | 97.32 | 96.43 | 95.98 | 97.89 |
Run 7 | 95.39 | 96.21 | 96.44 | 96.55 |
Run 8 | 96.32 | 95.93 | 96.09 | 97.30 |
Run 9 | 95.78 | 96.90 | 97.29 | 96.51 |
Run 10 | 95.84 | 95.66 | 96.43 | 97.10 |
Average | 95.78 | 96.15 | 96.43 | 96.68 |
The current research work developed an effective MBWO-CNN model for diagnosis and classification of COVID-19. The input image was first preprocessed in which the image was adjusted to a fixed size. Then, feature extraction was performed for the preprocessed image. Hyperparameter tuning process helped in the selection of initial hyperparameters of CNN model. Finally, the feature vectors were classified and the images were categorized into corresponding class labels,