Classification Of X-ray Images Research Articles

A Survey on Deep Learning in COVID-19 Diagnosis.

According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

Detection of COVID-19 in X-ray Images Using Densely Connected Squeeze Convolutional Neural Network (DCSCNN): Focusing on Interpretability and Explainability of the Black Box Model.

The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019. Although this disease is under control nowadays, yet it is still affecting people in many countries. The traditional way of diagnosis is time taking, less efficient, and has a low rate of detection of this disease. Therefore, there is a need for an automatic system that expedites the diagnosis process while retaining its performance and accuracy. Artificial intelligence (AI) technologies such as machine learning (ML) and deep learning (DL) potentially provide powerful solutions to address this problem. In this study, a state-of-the-art CNN model densely connected squeeze convolutional neural network (DCSCNN) has been developed for the classification of X-ray images of COVID-19, pneumonia, normal, and lung opacity patients. Data were collected from different sources. We applied different preprocessing techniques to enhance the quality of images so that our model could learn accurately and give optimal performance. Moreover, the attention regions and decisions of the AI model were visualized using the Grad-CAM and LIME methods. The DCSCNN combines the strength of the Dense and Squeeze networks. In our experiment, seven kinds of classification have been performed, in which six are binary classifications (COVID vs. normal, COVID vs. lung opacity, lung opacity vs. normal, COVID vs. pneumonia, pneumonia vs. lung opacity, pneumonia vs. normal) and one is multiclass classification (COVID vs. pneumonia vs. lung opacity vs. normal). The main contributions of this paper are as follows. First, the development of the DCSNN model which is capable of performing binary classification as well as multiclass classification with excellent classification accuracy. Second, to ensure trust, transparency, and explainability of the model, we applied two popular Explainable AI techniques (XAI). i.e., Grad-CAM and LIME. These techniques helped to address the black-box nature of the model while improving the trust, transparency, and explainability of the model. Our proposed DCSCNN model achieved an accuracy of 98.8% for the classification of COVID-19 vs normal, followed by COVID-19 vs. lung opacity: 98.2%, lung opacity vs. normal: 97.2%, COVID-19 vs. pneumonia: 96.4%, pneumonia vs. lung opacity: 95.8%, pneumonia vs. normal: 97.4%, and lastly for multiclass classification of all the four classes i.e., COVID vs. pneumonia vs. lung opacity vs. normal: 94.7%, respectively. The DCSCNN model provides excellent classification performance consequently, helping doctors to diagnose diseases quickly and efficiently.

Defects recognition of strain clamps based on attentional enhancement mechanism

The crimping quality of tension clamp in overhead transmission line is directly related to the safe and stable operation of transmission line. existing detection methods are low efficiency or availability and developing a timely and effective defect detecting method for strain clamp is a top priority. An X-ray image classification algorithm based on the attentional enhancement mechanism is proposed to identify the defects of different types of strain clamps. DenseNet structure is used as the backbone network to multiplex the X-ray image features, SENet channel is used to enhance the attention to the defect site, and PSANet is used to replace the 3×3 convolution layer in DenseBlock in DenseNet to highlight the features of defects at different scales. Research shows that the average recognition accuracy of the proposed algorithm is 88.1%, which is better than the existing methods.

Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network

The effect of segmentation on lung X-ray image classification has been analyzed in this study. The 150 lung x-ray images in this study were separated into 78 as training data, 30 as validation data, and 42 as testing in three categories: normal lungs, effusion lungs, and cancer lungs. In pre-processing, the images were modified by adaptive histogram equalization to improve image quality and increase image contrast. The segmentation aims to mark the image by contouring the lung area obtained from the thresholding and some morphological manipulation processes such as filling holes, area openings, and labelling. Image classification uses Convolutional Neural Network (CNN) with five convolution layers, an Adam optimizer, and 30 epochs. The segmentation effect is analyzed by comparing the classification performance of the segmented and unsegmented images. In the study, the unsegmented X-ray image dataset classification reached an overall accuracy of 59.52% in the network testing process. The segmented X-ray image dataset obtained greater accuracy, 73.81%. It indicated that the segmentation process could improve network performance because the input pattern of the segmented image is easier to classify. Furthermore, the segmentation technique in the study can be one of the alternatives to developing image classification technologies, especially for medical image diagnosis. Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network.

DETECTION OF COVID-19 WITH CHEST X-RAY

In order to speed up finding of causes of COVID-19 illness, this study developed novel diagnostic platform using profound convolutional neural network (CNN) helping radiologists diagnose COVID-19 pneumonia beside non-COVID-19 pneumonia in patient in Middle more Hospital. As the name suggests, crucial objective of our research is to produce a chest X-ray image classification program which could properly identify a scan's categorization as either "normal," "viral pneumonia," or "COVID-19." Using X-rays, we will train an image classifier to determine whether or not a person has COVID-19. In this data set, there are over 3000 chest X-ray pictures categorized in normal, viral, as well as COVID-19. A picture classifying system which properly identifies which of three categories Chest X-Ray scan corresponds with is purpose of this investigation.

Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images

In this era of Coronavirus disease 2019 (COVID-19), an accurate method of diagnosis with less diagnosis time and cost can effectively help in controlling the disease spread with the new variants taking birth from time to time. In order to achieve this, a two-dimensional (2D) tunable Q-wavelet transform (TQWT) based on a memristive crossbar array (MCA) is introduced in this work for the decomposition of chest X-ray images of two different datasets. TQWT has resulted in promising values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) at the optimum values of its parameters namely quality factor (Q) of 4, and oversampling rate (r) of 3 and at a decomposition level (J) of 2. The MCA-based model is used to process decomposed images for further classification with efficient storage. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. The average accuracy values achieved for the processed chest X-ray images classification in the small and large datasets are 98.82% and 94.64%, respectively which are higher than the reported conventional methods based on different models of deep learning techniques. The average accuracy of detection of COVID-19 via the proposed method of image classification has also been achieved with less complexity, energy, power, and area consumption along with lower cost estimation as compared to CMOS-based technology.

Application of unsupervised adversarial learning in radiographic testing of aeroengine turbine blades

The application of deep learning to X-ray image classification and recognition has attracted increasing attention in intelligent radiographic testing. In this study, we developed a defect detection method for X-ray images of aeroengine turbine blades using a novel unsupervised adversarial learning strategy. A defect detection model is proposed based on a generative adversarial network (GAN), which consists of a generator network, a discriminator network, and an extra encoder network. Employing an encoder–decoder–encoder subnetwork in the generator network enables the model to map an input image to a low-dimensional encoding vector, which is subsequently used to reconstruct the input image. The extra encoder network mapped the reconstructed image to a new encoding vector. Model training is implemented with non-defective X-ray image samples by minimising the distance between the original input image and the generated image (reconstruction loss) and the encoding vector distance (encoding loss). Defect detection is achieved using a generator network that can reconstruct non-defect samples but cannot reconstruct those with defects well. The effects of input image size, number of encoding nodes, and type of reconstruction loss on the performance of the defect detection model were studied. Consequently, the proposed model with an input image size of 128 × 128, 600 encoding nodes, and L2 reconstruction loss can achieve a detection accuracy of 0.911. This study proposes a method for developing intelligent radiographic testing techniques for the quality management of aeroengines.

HOG and Haralick Feature Extractions with Machine Learning Methods for Covid-19 X-Ray Image Classification

HOG and Haralick Feature Extractions with Machine Learning Methods for Covid-19 X-Ray Image Classification

Chest X-ray Image Classification for COVID-19 diagnoses

Background: Radiologists used chest radiographs to detect coronavirus disease 2019 (COVID-19) in patients and determine the severity levels. The COVID-19 cases were grouped into five classes, each receiving different treatments. An intelligent system is needed to advance the detection and identify vector features of X-ray images with a quality that is too poor to be read by radiologists. Deep learning is an intelligent system that can be used in this case. Objective: The current study compares the classification and accuracy of detection methods with two, three dan five classes. Methods: Deep learning can classify visual geometry group VGG 19 architectures with 1000 classes. The classification of the five classes' convolutional neural network (CNN) underwent model validation with a confusion matrix to produce accuracy and class values. The system could then diagnose patients’ examinations by radiology specialists. Results: The results of the five-class method showed 98% accuracy, the three-class method showed 99.99%, and the two-class showed 99.99%. Conclusion: It can be concluded that using the VGG 19 model is effective. This system can classify and diagnose viruses in patients to assist radiologists by reading the images. Keywords: COVID-19, CNN, Classification, Deep Learning

COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning

The infectious coronavirus disease-19 (COVID-19) is a viral disease that affects the lungs, which caused great havoc when the epidemic rapidly spread around the world. Polymerase chain reaction (PCR) tests are conducted to screen for COVID-19 and respond to quarantine measures. However, PCR tests take a considerable amount of time to confirm the test results. Therefore, to supplement the accuracy and quickness of a COVID-19 diagnosis, we proposed an effective deep learning methodology as a quarantine response through COVID-19 chest X-ray image classification based on domain extension transfer learning. As part of the data preprocessing, contrast limited adaptive histogram equalization was applied to chest X-ray images using Medical Information Mart for Intensive Care (MIMIC)-IV obtained from the Beth Israel Deaconess Medical Center. The classification of the COVID-19 X-ray images was conducted using a pretrained ResNet-50. We also visualized and interpreted the classification performance of the model through explainable artificial intelligence and performed statistical tests to validate the reliability of the model. The proposed method correctly classified images with 96.7% accuracy, an improvement of about 9.9% over the reference model. This study is expected to help medical staff make an integrated decision in selecting the first confirmed case and contribute to suppressing the spread of the virus in the community.

A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms.

The classification of medical images is significant among researchers and physicians for the early identification and clinical treatment of many disorders. Though, traditional classifiers require more time and effort for feature extraction and reduction from images. To overcome this problem, there is a need for a new deep learning method known as Convolution Neural Network (CNN), which shows the high performance and self-learning capabilities. In this paper,to classify whether a chest X-ray (CXR) image shows pneumonia (Normal) or COVID-19 illness, a test-bed analysis has been carried out between pre-trained CNN models like Visual Geometry Group (VGG-16), VGG-19, Inception version 3 (INV3), Caps Net, DenseNet121, Residual Neural Network with 50 deep layers (ResNet50), Mobile-Net and proposed CNN classifier. It has been observed that, in terms of accuracy, the proposed CNN model appears to be potentially superior to others. Additionally, in order to increase the performance of the CNN classifier, a nature-inspired optimization method known as Hill-Climbing Algorithm based CNN (CNN-HCA) model has been proposed to enhance the CNN model’s parameters. The proposed CNN-HCA model performance is tested using a simulation study and contrasted to existing hybridized classifiers like as Particle Swarm Optimization (CNN-PSO) and CNN-Jaya. The proposed CNN-HCA model is compared with peer reviewed works in the same domain. The CXR dataset, which is freely available on the Kaggle repository, was used for all experimental validations. In terms of Receiver Operating Characteristic Curve (ROC), Area Under the ROC Curve (AUC), sensitivity, specificity, F-score, and accuracy, the simulation findings show that the CNN-HCA is possibly superior than existing hybrid approaches. Each method employs a k-fold stratified cross-validation strategy to reduce over-fitting.

Computer aided detection of tuberculosis using two classifiers.

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both microbiologist and Radiologist and can lead to miss-diagnosis. The main objective of this study is to addressed these challenges by employing Computer Aided Detection (CAD) using Artificial Intelligence-driven models which learn features based on convolution and result in an output with high accuracy. In this paper, we described automated discrimination of X-ray and microscopic slide images of tuberculosis into positive and negative cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East university hospital and Kaggle repository. For classification of tuberculosis and healthy microscopic slide using AlexNet+Softmax, the model achieved accuracy of 98.14%. For classification of tuberculosis and healthy microscopic slide using AlexNet+SVM, the model achieved 98.73% accuracy. For classification of tuberculosis and healthy chest X-ray images using AlexNet+Softmax, the model achieved accuracy of 98.19%. For classification of tuberculosis and healthy chest X-ray images using AlexNet+SVM, the model achieved 98.38% accuracy. The result obtained has shown to outperformed several studies in the current literature. Future studies will attempt to integrate Internet of Medical Things (IoMT) for the design of IoMT/AI-enabled platform for detection of Tuberculosis from both X-ray and Microscopic slide images.

IEViT: An enhanced vision transformer architecture for chest X-ray image classification

Background and Objective: Chest X-ray imaging is a relatively cheap and accessible diagnostic tool that can assist in the diagnosis of various conditions, including pneumonia, tuberculosis, COVID-19, and others. However, the requirement for expert radiologists to view and interpret chest X-ray images can be a bottleneck, especially in remote and deprived areas. Recent advances in machine learning have made possible the automated diagnosis of chest X-ray scans. In this work, we examine the use of a novel Transformer-based deep learning model for the task of chest X-ray image classification.Methods: We first examine the performance of the Vision Transformer (ViT) state-of-the-art image classification machine learning model for the task of chest X-ray image classification, and then propose and evaluate the Input Enhanced Vision Transformer (IEViT), a novel enhanced Vision Transformer model that can achieve improved performance on chest X-ray images associated with various pathologies.Results: Experiments on four chest X-ray image data sets containing various pathologies (tuberculosis, pneumonia, COVID-19) demonstrated that the proposed IEViT model outperformed ViT for all the data sets and variants examined, achieving an F1-score between 96.39% and 100%, and an improvement over ViT of up to +5.82% in terms of F1-score across the four examined data sets. IEViT’s maximum sensitivity (recall) ranged between 93.50% and 100% across the four data sets, with an improvement over ViT of up to +3%, whereas IEViT’s maximum precision ranged between 97.96% and 100% across the four data sets, with an improvement over ViT of up to +6.41%.Conclusions: Results showed that the proposed IEViT model outperformed all ViT’s variants for all the examined chest X-ray image data sets, demonstrating its superiority and generalisation ability. Given the relatively low cost and the widespread accessibility of chest X-ray imaging, the use of the proposed IEViT model can potentially offer a powerful, but relatively cheap and accessible method for assisting diagnosis using chest X-ray images.

Periapical dental X-ray image classification using deep neural networks.

This paper studies the problem of detection of dental diseases. Dental problems affect the vast majority of the world's population. Caries, RCT (Root Canal Treatment), Abscess, Bone Loss, and missing teeth are some of the most common dental conditions that affect people of all ages all over the world. Delayed or incorrect diagnosis may result in mistreatment, affecting not only an individual's oral health but also his or her overall health, thereby making it an important research area in medicine and engineering. We propose a pipelined Deep Neural Network (DNN) approach to detect healthy and non-healthy periapical dental X-ray images. Even a minor enhancement or improvement in existing techniques can go a long way in providing significant health benefits in the medical field. This paper has made a successful attempt to contribute a different type of pipelined approach using AlexNet in this regard. The approach is trained on a large dataset of 16,000 dental X-ray images, correctly identifying healthy and non-healthy X-ray images. We use an optimized Convolutional Neural Networks and three state-of-the-art DNN models, namely Res-Net-18, ResNet-34, and AlexNet for disease classification. In our study, the AlexNet model outperforms the other models with an accuracy of 0.852. The precision, recall and F1 scores of AlexNet also surpass the other models with a score of 0.850 across all metrics. The area under ROC curve also signifies that both the false-positive rate and false-negative rate are low. We conclude that even with a big data set and raw X-ray pictures, the AlexNet model generalizes effectively to previously unseen data and can aid in the diagnosis of a variety of dental diseases.

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.

CheXGAT: A disease correlation-aware network for thorax disease diagnosis from chest X-ray images

Chest X-ray (CXR) imaging is one of the most common diagnostic imaging techniques in clinical diagnosis and is usually used for radiological examinations to screen for thorax diseases. In this paper, we propose a novel computer-aided diagnosis (CAD) system based on a hybrid deep learning model composed of a convolutional neural network (CNN) and a graph neural network (GNN). The system is intended to explore implicit correlations between thorax diseases to aid in the multilabel chest X-ray image classification task, which we term ‶CheXGAT‶. Specifically, the proposed CheXGAT framework comprises two main modules: an image representation learning (IRL) module and a graph representation learning (GRL) module. We employ the IRL module to learn high-level visual representation features from the CXR image. From the GRL module, the self-attention mechanism aggregates neighborhood features from the graphic structure to enhance the implicit correlation between thorax diseases. We adopted a data-driven method to create a disease correlation matrix that works on the message passing and aggregation process for the nodes in the GRL module. After end-to-end training, the GRL module enhances the correlation between thorax diseases to improve diagnosis performance. We performed experiments on the NIH Chest X-ray14 dataset, which contains 112,120 frontal-view radiographs; each image has multiple thorax disease labels. In the experimental results, the average AUC score of our proposed CheXGAT model reached 0.8266, and the AUC scores of Emphysema and Hernia reached 0.9447 and 0.9313, respectively. In addition, we visualized explanations via a gradient-based localization method in our proposed CheXGAT framework. Compared with previous studies, the experimental results show the competitive performance of our framework. Specifically, we propose a CAD system that uses a hybrid model to help radiologists identify CXR images from 14 chest diseases for clinical diagnosis.

Feature selection using regression mutual information deep convolution neuron networks for COVID-19 X-ray image classification

Chest radiography (CXR) image is usually required for lung severity assessment. However, chest X-rays in COVID-19 interpretation is required expert radiologists’ knowledge. This study aims to improve the COVID-19 X-ray image classification using feature selection technique by the regression mutual information deep convolution neuron networks (RMI Deep-CNNs). The dataset consists of 219 COVID-19, 500 viral pneumonias, and 500 normal chest X-ray images. CXR images were comprehensively pre-trained using DCNNs to extract the very large image features, then, the feature selection could reduce the complexity of a model and reduce the model overfitting. Therefore, the critical features were selected using regression mutual information followed by the fully connected with softmax layer for classification. For the classification of two alternative systems, these networks were compared (ResNet152V2 and InceptionV3). The classification performance for both schemes were 92.21%, 100%, 90% and 91.39%, 100%, 82.50%, respectively. In addition, RMI Deep-CNNs not only improve the accuracy but also reduce trainable features by over 80%. This approach tends to significantly improve the computation time and model accuracy for COVID‐19 classification.

Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies.

The global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. The need for the development of alternative smart diagnostic tools to combat the COVID-19 pandemic has become more urgent. In this study, a smart auxiliary framework based on machine learning (ML) is proposed; it can help medical practitioners in the identification of COVID-19-affected patients, among others with pneumonia and healthy individuals, and can help in monitoring the status of COVID-19 cases using X-ray images. We investigated the application of transfer-learning (TL) networks and various feature-selection techniques for improving the classification accuracy of ML classifiers. Three different TL networks were tested to generate relevant features from images; these TL networks include AlexNet, ResNet101, and SqueezeNet. The generated relevant features were further refined by applying feature-selection methods that include iterative neighborhood component analysis (iNCA), iterative chi-square (iChi2), and iterative maximum relevance–minimum redundancy (iMRMR). Finally, classification was performed using convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Moreover, the study exploited stationary wavelet (SW) transform to handle the overfitting problem by decomposing each image in the training set up to three levels. Furthermore, it enhanced the dataset, using various operations as data-augmentation techniques, including random rotation, translation, and shear operations. The analysis revealed that the combination of AlexNet, ResNet101, SqueezeNet, iChi2, and SVM was very effective in the classification of X-ray images, producing a classification accuracy of 99.2%. Similarly, AlexNet, ResNet101, and SqueezeNet, along with iChi2 and the proposed CNN network, yielded 99.0% accuracy. The results showed that the cascaded feature generator and selection strategies significantly affected the performance accuracy of the classifier.

Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques

The aim and objectives of the study are as follows: (i) to implement automated patch-based classification of hand X-ray images using modified pre-trained convolutional neural network (CNN) models; (ii) to develop a customized CNN model for automated feature extraction and classification of hand X-ray images and to compare the performance of customized CNN models with non-linear and linear kernels; (iii) to construct the hand crafted feature fusion (SIFT+ Customized CNN features) and categorize the normal and RA using Machine Learning classifiers. The model was trained on 75 images (10,000 patches) of hand radiographs and tested using 25 images (500 patches) that were not included in the training set. The accuracy of the modified pre-trained model GoogLeNet was 89% and the proposed custom model three achieved an accuracy of 95%. The sensitivity and specificity of GoogLeNet were 84% and 90% respectively. The custom model three attained the sensitivity and specificity as 95% and 94% respectively. Furthermore, when compared to the features extracted (SIFT + CNN) from the customized models, the custom3 model outperformed well for the classification of RA compared to ML classifiers. Thus a custom CNN-based computer-aided diagnostic tool can be used as an effective method for the detection of RA.

Topology optimization search of deep convolution neural networks for CT and X-ray image classification

Covid-19 is a disease that can lead to pneumonia, respiratory syndrome, septic shock, multiple organ failure, and death. This pandemic is viewed as a critical component of the fight against an enormous threat to the human population. Deep convolutional neural networks have recently proved their ability to perform well in classification and dimension reduction tasks. Selecting hyper-parameters is critical for these networks. This is because the search space expands exponentially in size as the number of layers increases. All existing approaches utilize a pre-trained or designed architecture as an input. None of them takes design and pruning into account throughout the process. In fact, there exists a convolutional topology for any architecture, and each block of a CNN corresponds to an optimization problem with a large search space. However, there are no guidelines for designing a specific architecture for a specific purpose; thus, such design is highly subjective and heavily reliant on data scientists’ knowledge and expertise. Motivated by this observation, we propose a topology optimization method for designing a convolutional neural network capable of classifying radiography images and detecting probable chest anomalies and infections, including COVID-19. Our method has been validated in a number of comparative studies against relevant state-of-the-art architectures.