key: cord-0766208-i0xevw7p authors: Nazir, Tahira; Nawaz, Marriam; Javed, Ali; Malik, Khalid Mahmood; Saudagar, Abdul Khader Jilani; Khan, Muhammad Badruddin; Abul Hasanat, Mozaherul Hoque; AlTameem, Abdullah; AlKathami, Mohammad title: COVID‐DAI: A novel framework for COVID‐19 detection and infection growth estimation using computed tomography images date: 2022-02-23 journal: Microsc Res Tech DOI: 10.1002/jemt.24088 sha: d4eca6bfc24e7161e9c21698362cc693e82c7908 doc_id: 766208 cord_uid: i0xevw7p The COVID‐19 pandemic is spreading at a fast pace around the world and has a high mortality rate. Since there is no proper treatment of COVID‐19 and its multiple variants, for example, Alpha, Beta, Gamma, and Delta, being more infectious in nature are affecting millions of people, further complicates the detection process, so, victims are at the risk of death. However, timely and accurate diagnosis of this deadly virus can not only save the patients from life loss but can also prevent them from the complex treatment procedures. Accurate segmentation and classification of COVID‐19 is a tedious job due to the extensive variations in its shape and similarity with other diseases like Pneumonia. Furthermore, the existing techniques have hardly focused on the infection growth estimation over time which can assist the doctors to better analyze the condition of COVID‐19‐affected patients. In this work, we tried to overcome the shortcomings of existing studies by proposing a model capable of segmenting, classifying the COVID‐19 from computed tomography images, and predicting its behavior over a certain period. The framework comprises four main steps: (i) data preparation, (ii) segmentation, (iii) infection growth estimation, and (iv) classification. After performing the pre‐processing step, we introduced the DenseNet‐77 based UNET approach. Initially, the DenseNet‐77 is used at the Encoder module of the UNET model to calculate the deep keypoints which are later segmented to show the coronavirus region. Then, the infection growth estimation of COVID‐19 per patient is estimated using the blob analysis. Finally, we employed the DenseNet‐77 framework as an end‐to‐end network to classify the input images into three classes namely healthy, COVID‐19‐affected, and pneumonia images. We evaluated the proposed model over the COVID‐19‐20 and COVIDx CT‐2A datasets for segmentation and classification tasks, respectively. Furthermore, unlike existing techniques, we performed a cross‐dataset evaluation to show the generalization ability of our method. The quantitative and qualitative evaluation confirms that our method is robust to both COVID‐19 segmentation and classification and can accurately predict the infection growth in a certain time frame. both COVID-19 segmentation and classification and can accurately predict the infection growth in a certain time frame. • We present an improved UNET framework with a DenseNet-77-based encoder for deep keypoints extraction to enhance the identification and segmentation performance of the coronavirus while reducing the computational complexity as well. • We propose a computationally robust approach for COVID-19 infection segmentation due to fewer model parameters. • Robust segmentation of COVID-19 due to accurate feature computation power of DenseNet-77. • A module is introduced to predict the infection growth of COVID-19 for a patient to analyze its severity over time. • We present such a framework that can effectively classify the samples into several classes, that is, COVID-19, Pneumonia, and healthy samples. ," 2020). The first victim of this pandemic was found in Wuhan, China. Initial sufferers of COVID-19 were usually found to be linked with the local wild animal market, which shows the probability of transferring this deadly disease to humans from the animals (Song et al., 2020) . The intense spread of the COVID-19 first outbreak in China and later all around the globe. The pandemic situation got worst that it has affected about 219 million people around the globe and caused the suspension of many political, social, financial, and sports events worldwide (Bagrov et al., 2021; Saba et al., 2021) . The distinguishing properties of COVID-19 are its ability to spread widely at a fast speed and are generally transferred directly from one person to another via respirational droplets. Moreover, it can be spread through the places, areas, and surfaces where the victims reside ("WHO, Q&A on Coronaviruses ," 2020). From the beginning till now, coronavirus is changing and showing its different variants from the one that was initially found in China like alpha, beta, delta, and so forth (Bollinger & Ray, 2021) . Under the prevailed conditions, the accurate and timely detection of coronavirus and quarantining the victims can play a vital role in prohibiting the spread of this disease Kaniyala Melanthota et al., 2020) . The symptoms of coronavirus vary from person to person, however, some of the major symptoms found in coronavirus patients are pneumonia in the lungs, cough, severe temperature, sore throat, tastelessness, extreme body weakness, headache, and fatigue (Hemelings et al., 2020) . The COVID-19 diagnosis methods are classified into three types namely molecular, serology tests, and medical imaging "Wikipedia, Covid-19 Testing -Wikipedia," 2020) . In the molecular test, a cotton swab is utilized to take the throat sample by swabbing its back. After this, a polymerase chain reaction (PCR) test is conducted on the obtained sample to detect the symptoms of the virus. The PCR test confirms the presence of COVID-19 in a person by identifying two unique SARS-CoV-2 genes, however, the PCR test can only detect the active cases of coronavirus. Another well-known molecular test is the antigenic test, where e lab-made antibodies are employed to search for antigens from the SARS-CoV-2 virus. Even though this test is faster than PCR, however, at the expense of decreased COVID-19 detection accuracy. On the other hand, serological testing is conducted to locate the antibodies generated to fight against the COVID-19 virus. For this examination, the blood sample of the patient is required to detect the infection level in the blood with mild or no signs (Emara et al., 2021; Kushwaha et al., 2020) . These antibodies are found anywhere in the body of a patient recovered from COVID-19. Both the PCR and the serological test are highly dependent on the availability of trained human experts and diagnostic kits, which results in slowing down the COVID-19 detection process. Besides molecular and serology-based COVID-19 detection approaches, the easier availability of medical imaging machines has enabled doctors to take images of chest structure using X-rays (radiography) or computed tomography (CT) scans. As COVID-19 causes pneumonia in the lungs of victims so, it can be easily detected by such images. With the assistance of radiologists for the ability to employ CT scans and X-rays to identify coronavirus, several approaches have been presented by the research community to utilize these samples. As, these images can be employed by using new technologies like magnetic resonance therapy to extend their usage for diagnostic purposes. Several techniques have been proposed by the researchers for COVID-19 detection and classification. Most of the existing works on COVID-19 are based on the classification of COVID-19 and normal images (Amin, Anjum, Sharif, Rehman, et al., 2021) . Arora et al. (2021) proposed a method to detect COVID-19 by employing the lung's CT-scan images. Initially, a residual dense neural network was applied to improve the resolution of input images. Then, a data augmentation approach was used to increase the diversity of samples. After this, several DL-based approaches namely VGG-16, XceptionNet, ResNet50, InceptionV3, DenseNet, and MobileNet were used to detect the COVID-19 affected samples. The approach in Arora et al. (2021) exhibits better COVID-19 detection accuracy, however, at the expense of enhanced economic cost. Panwar et al. (2020) introduced an approach to automatically recognize the COVID-19 affected patients from the X-ray and CT-scan images. The work (Panwar et al., 2020) presented a custom VGG-19 by introducing five extra layers to compute the deep features and perform the classification task. The method in Panwar et al. (2020) works well for COVID-19 detection, however, performance needs further improvements. Another DL-based approach namely Res-Net50V2 along with the feature pyramid network was proposed in Rahimzadeh et al. (2021) for COVID-19 recognition and classification. The work (Rahimzadeh et al., 2021) is robust to virus detection, however, exhibits a lower precision value. Turkoglu (2021) introduced a framework for the automated detection and categorization of COVID-19 victims. An approach namely Multiple Kernels-ELM-based Deep Neural Network was introduced for virus recognition, by employing chest CT scanning samples. Initially, a DL-based framework namely DenseNet201 was used to extract the deep keypoints from the suspected images. Then the Extreme Learning Machine classifier was trained over the extracted keypoints to measure the model performance. Finally, the resultant class labels were decided by utilizing the majority voting approach to give the final prediction. The method in Turkoglu (2021) shows better COVID-19 detection accuracy, however, requires extensive data for model training. Rahimzadeh and Attar (2020) introduced a framework to classify the COVID-19 and pneumonia-affected patients by employing Chest X-ray images. The approach worked by combining the deep features computed via Xception and Res-Net50V2 frameworks and performing the classification. The work in Rahimzadeh and Attar (2020) shows better COVID-19 recognition accuracy, however, at the expense of increased computational cost. Kadry et al. (2020) proposed a solution for COVID-19 recognition. In the first step, the Chaotic-Bat-Algorithm and Kapur's entropy (CBA + KE) approach was applied to improve the image quality. Then the bi-level threshold filtering was used to obtain the region of interest (ROIs). In the next step, the Discrete Wavelet Transform, Gray-Level Co-Occurrence Matrix, and Hu Moments methods were applied on the ROIs to obtain the image features. Then the computed key points were used to train several ML-based classifiers namely Naive Bayes, k-Nearest Neighbors, Decision Tree, Random Forest, and Support Vector Machine (SVM). The method in Kadry et al. (2020) obtains the best results for the SVM classifier, however, the technique requires further performance improvements. Mukherjee et al. (2021) introduced a nine layered CNN-tailored DNN for COVID-19 detection via employing both Chest X-ray and CT scan samples. The approach (Mukherjee et al., 2021) is computationally efficient, however, its detection accuracy needs further improvements. Similarly, a framework was proposed in Sedik et al. (2021) using both the Chest X-ray and CT scan images. This work used both a five-layered CNN framework and the long shortterm memory method for identifying the COVID-19 modalities. This approach exhibits better COVID-19 detection results, however, requires extensive data for model training. Existing methods have also employed various segmentation approaches to identify the COVID-19 segments from the input images. Voulodimos et al. (2021) presented an approach to segment the COVID-19 affected areas from the CT-scan samples. Two DLbased approaches namely fully connected CNNs and UNET were used to perform the segmentation task. It was concluded in Voulodimos et al. (2021) that UNET performs well than fully conned CNNs, however, performance still needs improvements. Another work was presented in Rajinikanth et al. (2020) to recognize the COVID-19 affected patients via employing lung CT scan images. After performing the preprocessing step, the Otsu function and Harmony-Search-Optimization were applied to improve the visual quality of samples. After this, the watershed segmentation approach was applied to extract the ROI. In the last step, the pixel values from the segmented samples of infected and the lung areas were used to measure the infection growth. The technique ) is robust to virus detection, however, unable to detect the COVID-19 virus from the X-ray images of the patients. In Gao et al. (2021) , an approach performing both the segmentation and classification of COVID-19 infection was presented. The Unet framework was employed to perform the lung segmentation. After this, a dual-branch combination network (DCN) was introduced to perform slice-level segmentation and classification. Furthermore, a lesion attention unit was also introduced in DCN to enhance the COVID-19 detection accuracy. The work (Gao et al., 2021) improved the coronavirus segmentation and classification accuracy, however, suffering from high computational cost. Shah et al. (2021) presented an approach for recognizing COVID-19 modalities via employing the CT scan images of victims. The work introduced a new model namely CTnet-10 to classify the healthy and affected persons. The work (Shah et al., 2021) is computationally efficient, however, performance needs further enhancements. Ter-Sarkisov (2021)) presented a DL-based framework namely Mask-RCNN for COVID-19 detection with two base networks namely ResNet18 and ResNet34, and exhibited better virus recognition performance. However, the method in Ter-Sarkisov (2021)) may not show better detection performance for samples with intense light variations. In de Vente et al. (2020)), a 3D-CNN framework has been proposed for COVID-19 recognition and segmentation. The work (de Vente et al., 2020) exhibits better COVID-19 modalities detection performance, however, it is evaluated over a small dataset. Accurate and timely detection, segmentation, and classification of COVID-19 is still a complex job because of its varying nature. Particularly, in winter when multiple viral infections spread, differentiating COVID from pneumonia is challenging. Moreover, the difference in the size, shape, location, and volume of coronavirus further complicates the detection process. Furthermore, the samples can contain noise, blurring, light, and color variations which also cause to degrade the recognition accuracy of existing systems. These challenging conditions often make it difficult for the physicians to correctly differentiate between Pneumonia and COVID-19. In addition, there is a lack of ability of such a system that can not only segment the COVID-19 from the lungs samples but also predict the infection growth estimation over a certain period. In addition, predicting the infection growth can help the practitioners to understand the severity level of COVID-19 with time. Therefore, there is an urgent need to develop a unified COVID diagnostic tool capable of segmenting the COVID lesions, estimating their infection growth over a certain time frame, and can assist to categorize the samples into COVID-19, Pneumonia, and normal classes. To overcome the problems of existing approaches, we have presented a DL-based approach named DenseNet-77-based UNET. Moreover, it is important to mention that according to the best of our knowledge, this is the first attempt to estimate the infection growth of COVID lesion segments over a certain period. Further, existing methods have not evaluated their methods under a cross dataset setting, thus, unable to prove the generalizability of their methods for COVID detection on two completely different and diverse datasets. Our work has the following main contributions: • We present an improved UNET framework with a DenseNet-77-based encoder for deep keypoints extraction to enhance the identification and segmentation performance of the coronavirus while reducing the computational complexity as well. • We propose a computationally robust approach for COVID-19 infection segmentation due to fewer model parameters. • Robust segmentation of COVID-19 due to accurate feature computation power of DenseNet-77. • A module is introduced to predict the infection growth of COVID-19 for a patient to analyze its severity over time. • We present such a framework that can effectively classify the samples into several classes, that is, COVID-19, Pneumonia, and healthy samples. • Rigorous experimentation was performed including the crossdataset evaluation to prove the efficacy of the presented technique. The rest of the manuscript is structured as: Section 2 contains the introduced work. We have discussed the test results of the proposed method in Section 3, while the conclusion along with the future work is demonstrated in Section 4. In this work, we have presented a novel approach for the segmentation and classification of COVID-19 namely UNET with DenseNet-77 as base network. Moreover, a module has been introduced to estimate the infection growth of COVID-19 for a physician to assist in determining the criticality of infection over time. The proposed solution comprises four main steps: (i) data preparation, (ii) model training, (iii) infection growth estimation, and (iv) classification. In the data preparation step, the COVID-19 data is preprocessed to make it appropriate for the training procedure. While in the next step, the DenseNet-77-based UNET framework is trained over the prepared data to segment and classify the COVID-19-affected samples. Then, the difference of detected samples from a single patient is taken to estimate the infection growth over a certain time. We introduced an improved UNET along with the DenseNet-77 as its base backbone for feature extraction. The model utilizes the dense connections to recapture pixel information that has been missed because of using the pooling operations. The UNET model consists of two parts namely encoder and decoder. In this work, the encoder part employs DenseNet-77 to calculate the representative set of keypoints from the input data, and the decoder utilizes the UNET original decoder. We have also performed the classification task to categorize the suspected samples into three classes, that is, COVID, pneumonia, and normal. To accomplish this task, we have used the DenseNet-77 framework to extract the image features and categorize them into their respective classes. The entire workflow of the introduced is exhibited in Figure 1 . In the preprocessing phase, we have applied the image enhancement technique, that is, Contrast-limited adaptive histogram equalization (CLAHE) (Reza, 2004) for contrast enhancement. CLAHE method highlights the important information by removing the unnecessary details and gives a better representation of the input image. After that, we prepared the available dataset according to the necessity of the model training by the generation of annotations using the available ground truths. A discriminative set of image features is mandatory to accurately segment and classify the COVID-19 affected regions from the suspected samples. Whereas the following causes complicate the process of calculating efficient key points from the images: (i) the large-sized feature vector can cause the model to face the overfitting problem, and (ii) the employment of a small feature vector can cause the model to miss to learn the important aspect of the object architecture, that is, the structural, size and color variations of COVID-19 virus. To cope with such challenges, it is essential to present a fully automated feature extraction framework without employing the handcrafted feature computation frameworks. The approaches with hand-coded feature computation networks are unable to perform well for COVID-19 segmentation and classification due to the abrupt change in its shape, size, volume, and texture. So, to overcome the aforementioned problems, we have utilized a DL-based network namely UNET with DenseNet-77 as a feature extractor to directly calculate the robust set of image features. The convolution filters of UNET compute the key points of the input image by examining its structure. In history, several segmentation techniques have been proposed like edge detection , region-based (Raja et al., 2009) , and pixel-based methods (Zaitoun & Aqel, 2015) . However, the motivation for selecting the UNET model over them is that these approaches are unable to deal with the advanced challenges of image segmentation like the extensive changes in the light, color, and size of the COVID-19 infected region. Furthermore, these approaches are economically expensive and are not well suited to real-world object recognition problems. The UNET framework is more powerful in comparison to the traditional models, in terms of both architecture and pixel-based image segmentation formed from the CNN layers. Moreover, it requires less data for model training which gives it a computational advantage from its peered approaches. The UNET framework comprises two modules namely contracting path or encoder and an expansive path or decoder. The purpose of the encoder is to compute the deep features of the suspected samples while the second module is the symmetric expanding path which is utilized to perform the accurate localization using transposed convolutions. The traditional UNET encoder includes the repetitive application of 3  3 convolutions, where each layer contains the Relu activation function (ReLU) along with a 2  2 max-pooling layer having a stride rate of 2 for down-sampling. In each down-sampling step, the keypoints channels are doubled. While on the decoder side, with each up-sampling step, the feature map corresponds to a 2  2 upconvolution layer that halves the feature channels and performs con- (Albahli, Nawaz, et al., 2021; Albahli, Nazir, et al., 2021) . DenseNet approach is capable of exhibiting the complex image transformations accurately which can help to overcome the issue of missing object location information to precisely locate it. Furthermore, DenseNet supports the key-points communication process and increases their reuse which empowers it to be employed for COVID-19 segmentation and classification. So, in our proposed solution, we employed the DenseNet-77-encoder based UNET framework for extracting the deep features of the suspected sample. Figure 2 shows thestructuraldetailsoftheemployedUNETframework. The encoder or contracting module contains a CNN for calculating the deep keypoints from the input samples. After this, it minimizes the computed keypoints by down-sampling the image to acquire the advanced image information. The traditional encoder of the UNET framework generates the image feature maps by dropping the resolution of the suspected sample. Therefore, our work employs the DenseNet-77 as a feature extractor in the encoder part which is known as a stable framework with depth, width, and resolution. Furthermore, the benefit of employing the DenseNet-77-based encoder is that it enhances the training procedure by utilizing pre-trained ImageNet weights and contains fewer parameters to obtain robust performance.The Densenet-77 has mainly two differences from conventional DenseNet: (i) Densenet-77 has fewer model parameters as the traditional DenseNet contains 64 feature channels, while Densenet-77 comprises 32 feature channels on the first convolution layer, with the kernel size of 3  3 as a substitute of 7  7. (ii) Inside each DB, the layers are adjusted to overcome the economic burden. The network structural description of employed DenseNet-77 is demonstrated in Table 1 which shows the layer's name employed for features extraction to implement the advanced processing by the Custom-UNet. Moreover, the visual representation of DenseNet-77 is given in Figure 3 , where it can be seen that DB is the essential component of DenseNet. In Figure 3 , for n À 1 layers, x  x  y 0 presented the features maps (FPs), and x and y 0 show the size of FPs and the number of channels, respectively. A non-linear transformation presented by h(.) containing several methods named Batch Normalization, Rectified linear unit (Relu), and a 1  1 convolution layer (ConL) is introduced to reduce the channels. Furthermore, a 3  3 ConvL is applied for performing the features reformation. The dense connections among the consecutive layers are presented by the long-dashed arrow while x  x  (y 0 + 2y) is the output value from the n + 1 layer. In the DenseNet-77 the wide dense links rise the FPs significantly, hence, the transition layer (TrL) is used after each DB to minimize the feature size. To perform the classification task, we employed the DenseNet-77 model which is also used as a base network in the encoder part of the UNET approach. The detailed description of DenseNet-77 is given in Table 1 . We used the DenseNet-77 as an end-to-end framework, which computes the deep features and performs the classification task. We activated the softmax layer along with the fully connected layers at the end of the DenseNet-77 features extractor. We performed the transfer learning concept to fine-tune the proposed model and in-depth details of hyperparameters are given in Table 2 . In this section, we have demonstrated a comprehensive discussion of the obtained results for the COVID-19 segmentation, infection growth estimation, and classification. Furthermore, the description of employed datasets for both the segmentation and classification are also provided. The proposed approach is executed using Python and runs on an Nvidia GTX1070 GPU-based machine. For experiments, we have employed a dataset namely: COVID-19-20 which is publicly available (Stefano & Comelli, 2021 (Gunraj, 2020 To evaluate the introduced framework, we have utilized numerous standard performance measuring metrics, for example, Dice (D), accuracy, precision, recall, and AUC. We computed the accuracy, precision, recall and dice as follows: Precision Recall Moreover, we have calculated the error rate of infection growth estimation as follows: Here, TP, TN, FP, and FN are showing the true positive, true negative, false positive, and false negative, respectively. In this section, we have discussed the segmentation results of the presented technique by evaluating them in terms of several standard metrics. To design an accurate COVID-19 recognition model, it must be capa- We have conducted an experiment to check the segmentation accuracy of UNET with different base networks. For this reason, we have compared the DenseNet-77-based UNET framework with other base models for example with VGG-16 (Yu et al., 2016) , ResNet-50 (Han et al., 2016) , ResNet-101 (Canziani et al., 2016) , and DenseNet-121 (Solano-Rojas et al., 2020) and results are reported in Table 3 . From our case. Therefore, in terms of sensitivity and specificity, the presented framework achieves a performance gain of 11.7% and 23%, respectively. Similarly, in terms of training time, the presented approach outperforms the existing methods. The main reason for the better segmentation and computational performance of our work is that the techniques in Stefano and Comelli (2021) ) employ residual links with skip connections without using transition layers, which not only causes to increase the network parameters but also miss to learn the important image behaviors like the positions and structure of COVID lesions. While in comparison, the presented approach uses dense connections which assists the UNET model to learn a more reliable set of image features and increases its recall rate. Therefore, we can conclude that the proposed solution is more robust to latest techniques and reduces the computational complexity as well. In this section, we have discussed the patient-wise severity-rate pre- This analysis can assist the practitioners to determine the health condition of COVID-19-affected patients as to whether the victim is recovering or not with time. The presented technique exhibits the maximum and minimum error rates of 0% and 0.41%, respectively as shown in Figure 7 . The main reason for the effective performance of infection growth estimation is due to the effective segmentation of coronavirus areas by proposed UNET which shows the complex patient-wise data transformations accurately. After Moreover, we have plotted the F1-score along with the error rate in Figure 9 where the blue color is showing the class-wise F1-score and the rest portion is showing the error rates. Figure 9 is clearly showing that the model is relatively effective for coronavirus classification and shows an average error rate of 1.99%. Furthermore, our approach attains the accuracy values of 98.83%, 98.98%, and 99.84% for COVID-19, Pneumonia, and healthy samples, respectively. As the model shows robust class-wise classification results, so, we can say that it has a better recall rate due to the employed dense architecture. To further analyze the DenseNet-77 classification results, we have designed the confusion matrix as shown in Figure 10 . We have also plotted the AUC-ROC curves ( Figure 11 ) for all classes as it assists in better visualizing the performance of the multiclass classification problem. AUC-ROC curve is an important performance evaluation metric for assessing any classification model's performance. ROC presents the probability curve while the AUC shows the estimation of separability. Here, it demonstrates how much the presented framework can differentiate the various classes, that is, COVID-19-affected, normal, and pneumonia, respectively. As more the value proceeds towards 1, the more accurate is the model performance and Figure To evaluate the effectiveness of the presented work over the contemporary deep learning models, we have designed an experiment to compare the performance of our approach with several base models, that is, AlexNet (Krizhevsky et al., 2017) , GoogLeNet (Szegedy et al., 2015) , InceptionV3 (Szegedy et al., 2016) , VGG16 (Yu et al., 2016) , ShuffleNet (Zhang et al., 2018) , MobileNetV2 (Sandler et al., 2018) , ResNet18, ResNet50 (Han et al., 2016) , and ResNet101 (Han et al., 2016) . For a fair comparison, we have performed two types of analysis. Initially, we have compared the class-wise performance of our approach with the base models and the results are demonstrated in Table 6 . It is visible from In the second analysis, we have analyzed the classification performance of our method on the entire dataset with the base models in terms of precision, recall, and accuracy. The comparative results are shown in Figure 12 which is demonstrating the robustness of our approach. From Figure 12 it can be viewed that the GoogLeNet shows the minimum evaluation performance with the precision, recall, We have also tested the generalization ability of our approach by conducting an experiment on a cross dataset. The main purpose is to assess the performance of our approach on completely unknown sam- This work has proposed an effective framework for the automated segmentation, classification, and infection growth estimation of the COVID-19-affected samples. After the preprocessing step, we have employed the DenseNet-77-based UNET approach to segment the coronavirus region from the suspected sample. More specifically, we have introduced the DenseNet-77 as a base framework in the UNET model to compute the representative set of image features. The calculated key-points are segmented by the UNET model. After performing the segmentation task, we have calculated the patientwise infection growth computation of coronavirus-affected victims. Furthermore, we have trained the DenseNet-77 framework as an end-to-end network to perform the classification task. We have performed extensive experimentation over the two challenging datasets namely COVID-19-20 and COVIDx CT-2A for the segmentation and classification tasks evaluation. Furthermore, a crossdataset validation is performed to demonstrate the generalization capability of our approach. It is quite evident from the reported results, that our work is proficient in both coronavirus segmentation and classification. Furthermore, the inclusion of infection growth estimation for each patient over time can further assist the doctors to understand the varying behavior of this deadly disease. So, we can say that our work can facilitate the practitioner to better treat coronavirus-infected patients. In the future, we plan to extend our work to process the 3D images for further performance improvement. 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The authors declare no potential conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors. All data is available upon request ORCID Ali Javed https://orcid.org/0000-0002-1290-1477