Santos and Melin [5] have considered the importance of the development of the computer-intelligence technique for pneumonia classification, they utilized the ML approach for diagnosis and detection where the segmentation technique is used for the extraction of the area of interest from the chest X-ray. The experimental results reveal a mean accuracy of 95%. Tuncer et al. [6] used a traditional machine learning-based system. The size of the dataset that has been used was small and imbalanced. It contains (135 for COVID-19, 150 for pneumonia, and 150 for normal radiographs), and they achieved an accuracy rate of 97.01%. For pneumonia X-ray classification, Sharma et al. [7] designed a simple CNN model, and to solve the problem of scarcity of data they utilized data augmentation. The achieved accuracy was around 90.68%.

Danilov et al. [8] have tested four deep learning networks to evaluate the performance for pneumonia, and COVID-19 classification, the networks are EffecientNet B1, B2, MobileNetV2, and VGG16 from 2631 CXRs. When they used fine-tuning, the presented model reached an accuracy of 78% which is not a promising value. To improve the model performance, the authors utilize Grad-CAM, which presents guided attention to the chest part based on the segmentation of U-Net. This technique improved the COVID-19, pneumonia classification. In [9] , the authors used deep learning instead of the reverse transcription polymerase chain reaction method for COVID-19 identification because it was found that the algorithm is time-consuming even though deep learning is showing promising performance. The dataset consists of 13,569 X-ray images split into healthy, non-COVID-19 pneumonia and COVID-19 patients. The results show that this approach presents an excellent model with an accuracy of 93.9%, a COVID-19 sensitivity of 96.8%, and a positive prediction of 100%. The authors in [10] have collected a dataset of X-ray images of healthy and COVID-19 patients, this dataset become the biggest dataset. Next, they trained a CNN model, and they achieved a total accuracy of 87.88%. Bhattacharyya et al. [11] presented a deep learning method combining image segmentation and classification models to detect COVID-19, and health efficiently. They used segmentation on X-ray raw images by the conditional generative adversarial network (C-GAN) with the algorithm called pix to pix. After that, they put segmented images into the network for feature extraction, which is a combination of several deep neural networks such as VGG-16, VGG19, and DensNet-160. In the end, they used different ML techniques to classify extracted features such as SoftMax, SVM, and XG boost. They obtained the highest accuracy with the VGG-19 model at 96.6%.

Multi-task learning has proved its ability to work with classification and detection issues. Indeed, multi-tasking is a new pragmatism to solve these types of problems [12] . It can be used for different diseases using, for instance, chest X-ray images and CT scan images as input data. Indeed, in [13] , the authors have proposed an artificial neural network as a tool to detect lung diseases such as chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer. After a pre-processing of images to remove irrelevant data, statistical features are extracted like standard deviation, entropy, and mean. For the architecture, they used a feed-forward neural network to identify such diseases. The dataset is collected from Sasoon Hospital. In [14] , to diagnose CXR thoracic diseases, a new version of CNN was used, called Attention Guided convolutional neural network (AG-CNN), which is utilized to combine the global and local information to improve recognition performance. Authors in [15] have proposed an algorithm called chest-X-Net. This algorithm, with a CNN architecture, is composed of 121 layers. It takes an image of the chest as input and the result is the likelihood of thorax diseases. This approach (ChexNet) has been used successfully on 14 thoracic diseases. The authors in [16] have mentioned that the multi-task learning method utilizing deep neural networks was historically subdivided into two structures: soft parameter sharing and hard parameter sharing. In hard parameter sharing, the parameters set is typically divided into task-specific and shared parameters, whereas in soft parameter sharing each task has its own set of parameters, single head for each parameter set. Udeshani et al. [17] , proposed different approaches, where chest X-ray images are used to identify lung cancer. Using this approach, the achieved accuracy was 96% while using the feature-based technique the achieved accuracy was 88%. Al-Edhari et al. [18] used a deep learning algorithm by using X-ray images as input to identify the normal and abnormal classification of pneumonia disease. Liu et al. [19] used a defined multi-task architecture, which divided the feature space into a task space and a shared specific part. They mentioned that multi-task learning is a powerful technique able to enhance the performance of a single task with the aid of other tasks together [20] [21] . Hemdan et al. [22] developed many deep-learning approaches to classify CXR images for COVID-19 and non-COVID-19 patients. They introduce a novel approach namely COVIDX-Net to help doctors automatically detect the disease in CXR. They achieved good results with an accuracy of 90% by utilizing VGG16 and DenseNet201. In [23] , Yuxing Tang et al. presented a convolutional neural network based on attention-guided curriculum learning (AGCL) to increase the accuracy of localization and classification of thoracic diseases. In addition, they have exploited the use of a relevant attention-guided improvement framework to enhance the performance for both localization and classification. In [24] , the authors used deep convolutional neural networks to detect thoracic disease utilizing chest X-rays. This principle might have two drawbacks. Firstly, unmatched objects in all chest X-ray images may lead to unwanted noise. Secondly, it reduces the image resolution because of the scaled process. To solve both issues, the authors have proposed a new technique called a Segmentation-based Deep Fusion Network, which enhances field knowledge and high-resolution information for lung areas. Varshni et al. [25] proposed a deep learning model with a specific convolutional neural network for feature extraction with a transfer learning approach. Indeed, they provided a pneumonia identification system utilizing a Densely Connected Convolutional Neural Network’ (DenseNet-169). This model, composed of CNNs for feature extraction combined witha supervised classifier algorithm emerged as an optimal solution for detecting normal and abnormal X-ray chest images. Wesley et al. [26] utilized the Alexnet deep learning network with transfer learning. The dataset composed of 5659 images was utilized for training. It achieved 72% accuracy. In this paper transfer learning is used, CNN model is Alexnet. The last three layers of the Alexnet model were replaced by a fully connected layer. The rest of the layers are standard classification layers. The goal of the work presented in [2] is to use a multi-task deep learning methodology to identify patients with COVID-19. They employed their Inception Residual Recurrent Convolutional Neural Network with a transfer learning approach to detect COVID-19 disease using X-ray and CT scan images. The achieved accuracy for CT scan images was 98.78% and for X-ray images was around 84.67%. The authors in [27] described a new framework that relies on the encoder-decoder multi-task model. They have used the CXR real-world dataset which contains healthy, bacterial pneumonia, viral pneumonia, and COVID-19 pneumonia images. The average achieved accuracy was 87.9%. In [28] , the authors have proposed a multi-classification deep learning architecture to diagnose and classify four types of diseases including COVID-19, pneumonia, lung cancer, and normal cases. The dataset combines CT scans and CXR images. The results have shown that VGG19+CNN achieved the highest accuracy of 98.05%. Authors in [29] employed a multitask approach to consolidate both triage approaches and propose a deep convolutional neural network for identification and severity quantification of COVID-19 disease.

In this study, two different datasets have been used. The first data set is chest X-ray radiographs [30] . It contains 5856 files labeled (pneumonia vs. normal), which are split into the testing set and training set. Testing set with (234 Normal and 390 Pneumonia) and training set with (1349 normal and 3883 pneumonia). These images are stored in the format of JPEG. The resizing operation has been performed on these images. Before the operation, they had a width of 1233 and a height of 1460. After resizing they become 224 × 224 × 3 to fit the models used, all models have the input image with size 224 × 224 × 3 except inceptionv3 which has an image size of 150 × 150 × 3. These operations make this dataset useful for our research. The second dataset contains CT scans for COVID-19 [30] , with a total number of 8.055 files split and labeled as (5427 COVID and 2628 Non-COVID). They are not split into training and testing sets, therefore, we manually split them into 20% for the testing set and 80% for the training set. Figure 1 shows samples from both datasets.

Two different models of machine learning are proposed. Firstly, single-task deep

learning is used for the two disease classifications. Secondly, a multi-task deep

learning model for both diseases is developed. These two machine-learning models are evaluated on both CT scans for COVID-19 and X-ray images for pneumonia disease.

Four distinct state-of-the-art PCNN models, namely VGG-16, Inception-V3, Effi-cientNetB0, and ResNet-50, have been employed within the framework of a single-task model. These models were instantiated using Python’s machine-learning libraries and executed in the Google Colab environment. All these models have been retrained to find optimal weights that achieve better performances. The hyperparameters used for Re-seNet50 and EfficientNetB0 are shown in Table 1.

These networks were trained using 5 to 10 epochs, but to avoid overfitting, the training limit of 5 epochs will be used for multi-tasking. These models have a high performance with low overfitting. The batch size was chosen as 16 and to optimize the network parameter Adam optimizer was used with a learning rate of 0.0001. It decayed by 0.1 factor at the 5^th epoch. In addition, the dataset used is a balanced dataset which means an equal number of images for each label. In this experiment, there were 500 images for each label of the COVID-19 and pneumonia dataset. Table 2 and Table 3 show different achieved accuracies for

single-task learning. For the Pneumonia disease, the highest score is realized by the EffecientNetB0 model (96.50%). Whereas for the COVID-19 disease, the ResNet-50 model reached the highest accuracy at 99.5%. Therefore, to conclude this part, the highest accuracy performance for single-task learning was obtained with the architecture ResNet-50 up to 99.5%, and it will be used as PCNN in the MTL. Figure 4 illustrates a sample of accuracy during the training and validation phase with the loss function variation.

The model of a single-task has been enhanced by building multi-task frameworks. Single-task performance will be improved by using multi-task deep learning with its two main methods which are hard parameter sharing and soft parameter sharing. The most useful is hard parameter sharing, here it achieved an accuracy of 99.9%. However, with soft parameter sharing the performance was not promising. The same hyperparameters used in single-task learning have been followed with some slight differences. For instance, Adam optimizer was used, the batch size was 64, the learning rate with 0.001, and the number of epochs varied from 5 to 10. These parameters have been adjusted until the desired accuracy is achieved. Figure 5 and Figure 6 depict the training and validation accuracies with their corresponding loss functions.

In this work, we performed and compared the performances of different models. First, we compare the accuracy of pre-trained architectures (PCNNs) to determine the best one. It has been found that ResNet-50 outperforms the other PCNN models. This model has been chosen to be used in the multi-task approach for even more enhancement. Table 4 shows different achieved accuracies for COVID-19 and Pneumonia diseases. Further, the total number of parameters for each model is also mentioned. The second table (Table 5) is related to the achieved performance of multi-task learning using the RESNET-50 model,

where it is noticed that it has achieved the best accuracy under a hard parameter sharing structure, in comparison with the soft parameter sharing structure (see Table 4 and Table 5).

Moreover, comparisons of this work with others who have almost the same dataset with the same architectures are conducted. The models and the achieved accuracies are shown in Table 6 for single-task learning and Table 7 for multi-task learning approaches. Further, in this table, a comparison of the proposed scheme with their results is also displayed. The proposed models have outperformed their accuracies in both approaches single-task learning and multi-task learning as well. Therefore, we consider that we overcome their limitations and improve the accuracy of the classification. (See the comparison Table 6).

Furthermore, the achieved accuracies (see Table 5) are promising with a multi-task deep learning framework under the method of hard parameter sharing. The achieved accuracy is 99.9%, whereas with the second method, using soft parameter sharing, the performance achieved was up to 93.8% with the ResNet-50 model. In addition, the same work has been tested with EfficeintNetB0 since it achieves high accuracy in single-task learning for pneumonia disease, but it performs poor performance, and for this reason, this model has been ignored. We have used only the architecture of ResNet-50 since it achieved the highest result among the other architectures in single-task learning. We further improve this architecture using multi-task learning.

Figure 7 shows several prediction scores of the proposed method. The trained model clearly distinguishes Pneumonia, COVID-19 diseases, and normal.