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a Example of pulmonary opacities; b Normal chest radiography showing the main identifiable anatomical structures (LA left atrium, LV left ventricle, AD right atrium)
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Pneumonia is responsible for high infant morbidity and mortality. This disease affects the small air sacs (alveoli) in the lung and requires prompt diagnosis and appropriate treatment. Chest X-rays are one of the most common tests used to detect pneumonia. In this work, we propose a real-time Internet of Things (IoT) system to detect pneumonia in c...
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Pneumonia is an infection that inflames the air sacs in lungs and is one of the prime causes of deaths under the age of five, all over the world. Moreover, sometimes it became quite difficult to detect and diagnose pneumonia by just looking at the chest plain X-ray images. Therefore, we propose a hybrid deep convolutional neural network model (HDCN...
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... When dealing with large fields, this job becomes extremely difficult. To address this problem, many artificial intelligence disease detection techniques are being used [3][4][5][6][7][8][9][10][11]. ...
Plant diseases pose a significant threat to agricultural productivity, emphasizing the essential need for early detection and diagnosis. While recent deep learning approaches have made strides in plant disease detection, existing methods fall short in accuracy when handling low-resolution (LR) images, often encountered in field conditions due to limited lighting and variable weather. To address this problem, this research introduces a novel Adaptive scale Feature Extraction Super-Resolution Network (ASFESRN) for corn leaf disease detection. The proposed ASFESRN leverages a unique Feature Dismemberment and Integration Block (FDIB) and Adaptive scale Group Convolutional Network (ASGCN) modules, specifically designed to enhance the resolution of LR field images. Unlike previous group convolution methods, the ASGCN module simultaneously extracts spatial and channel information from the original feature map, overcoming limitations and preserving data transmission. The FDIB, incorporating a Channel Attention Module, three ASGCN blocks, and five convolution layers, further enhances the network’s performance in image super resolution. In comparison to existing methods, the ASFESRN demonstrates superior performance in image super resolution, exhibiting improved PSNR, SSIM. The obtained super-resolved (SR) images are then fed into the Disease Detection Block (DDB) for accurate identification of corn leaf diseases. The proposed ASFESRN model underwent extensive training and testing on the PlantVillage Corn Leaf Disease dataset, encompassing various super resolution scaling factors. Results showcase the effectiveness of the proposed model, with accuracies surpassing existing methods for Diverse super resolution scaling factors. The proposed ASFESRN achieves accuracies of 99.7402%, 98.4805%, and 98.9610%, respectively for super resolution scaling factors (𝝬2, 𝝬4, 𝝬6), offering a robust solution for real-time, accurate disease diagnosis in challenging field conditions. This innovative approach equips farmers with early and accurate diagnosis of corn leaf diseases, aiding in effective crop protection.
... A real-time IoT system for identifying pneumonia in chest X-ray images was suggested [55]. Twelve distinct CNN architectures trained on ImageNet were explored by coupling with other machine learning techniques. ...
Computed tomography (CT) scans are widely used to diagnose lung conditions due to their ability to provide a detailed overview of the body's respiratory system. Despite its popularity, visual examination of CT scan images can lead to misinterpretations that impede a timely diagnosis. Utilizing technology to evaluate images for disease detection is also a challenge. As a result, there is a significant demand for more advanced systems that can accurately classify lung diseases from CT scan images. In this work, we provide an extensive analysis of different approaches and their performances that can help young researchers to build more advanced systems. First, we briefly introduce diagnosis and treatment procedures for various lung diseases. Then, a brief description of existing methods used for the classification of lung diseases is presented. Later, an overview of the general procedures for lung disease classification using machine learning (ML) is provided. Furthermore, an overview of recent progress in ML-based classification of lung diseases is provided. Finally, existing challenges in ML techniques are presented. It is concluded that deep learning techniques have revolutionized the early identification of lung disorders. We expect that this work will equip medical professionals with the awareness they require in order to recognize and classify certain medical disorders.
... It has been demonstrated by the study that the most appropriate model for the diagnosis of pneumonia from 6000 chest radiographs of pediatric patients is the model using the RBF kernel with an SVM classifier based on the VGG199 architecture. The classification accuracy of this study was calculated as 96.47% [47]. Gupta et al. (2022) applied the Neural Architecture Search (NAS) model and classified chest X-ray images of pediatric patients aged 1-5 years, taken from Guangzhou Women's and Children's Medical Center, as pneumonia/normal. ...
In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or pediatric patients. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal death, early and accurate diagnosis of respiratory diseases in neonates is crucial. For this reason, our study aims to detect the presence of respiratory disorders through the developed deep-learning approach using chest X-ray images of patients hospitalized in the Neonatal Intensive Care Unit. Accordingly, the enhanced version of C+EffxNet, the new hybrid deep learning model, is designed to predict respiratory disorders in neonates. In this version, the features selected by PCA are combined as 100, 200, and 300, then the binary classification process was carried out. In the study, the accuracy and kappa value were obtained as 0.965, and 0.904, respectively before feature merging, while these values were obtained as 0.977, and 0.935 after feature merging. This method, which was developed for the diagnosis of respiratory disorders in neonates, was also subsequently applied to a chest X-ray dataset that is frequently used in the literature for the diagnosis of pediatric pneumonia. For this data set, while the accuracy was 0.992, the kappa value was 0.982. The results obtained confirm the success of the proposed method for both datasets.
... In addition, A. baumannii biofilm-forming capacity exerts its effect on resistance phenotypes [8]. Pneumonia is an acute inflammatory response in the lungs that affects the air sacs (alveoli), followed by swelling, reducing oxygen delivery and gas exchange [9]. Every year, over 450 million people, or 7% of the world's population, get pneumonia, and about 4 million people die from it [10]. ...
Acinetobacter baumannii is a worldwide health issue in terms of its high antibiotic resistance and ability to form biofilms. Nanoparticles (NPs) with high biocompatibility, high penetrating ability, and low medication dose can successfully treat the antibiotic-resistant infections. In this research, the anti-biofilm activity of niosomes containing minocycline and gallium nitrate (GaN) against A. baumannii biofilm was determined. In order to improve their anti-biofilm properties, minocycline and GaN were encapsulated in niosomes as biocompatible drug carriers. The niosomes' size, zeta potential, shape, stability, drug entrapment efficacy, drug release pattern and antibacterial activity were assessed. Several clinical samples were isolated from the lungs of patients hospitalized at Loghman hospital, Tehran, Iran. The biofilm formation of most lethal clinical isolates of A. baumannii was analyzed. The pneumonia model was generated by intranasally administering A. baumannii suspension to anesthetized mice whose immune systems was compromised twice by cyclophosphamide. Lung infection of the mouse with A. baumannii was confirmed using PCR. After treatment, the lungs were excised under sterile conditions and stained with hematoxylin and eosin (H&E) to determine histological symptoms, inflammation and intercellular secretions. The niosomes contained minocycline and GaN had an average size of 230 nm and a zeta potential of − 40 mV, respectively. The percentage of drug entrapment and delayed drug release was both high in niosomal formulations. Niosomes containing minocycline and GaN dispersed 1, 3 and 5 day old biofilms. The mice given the combination of two compounds required less time to be treated than the animals given the single medication (minocycline). The minocycline& GaN-loaded niosomes could be considered as promising candidates to treat the infections caused by A. baumannii biofilm.
... The result shows the second mechanism provides the highest accuracy yet this method classifies a single disease. In 2021 das Chagas et al. [25] established an IoT-based novel technique to detect pneumonia in children using X-ray scans. In this model, 12 pre-trained CNN networks are chosen as feature extractors, and the approaches were integrated with classification learning models. ...
... This concept can be extended to other lung diseases in order to improve their internal accuracy performance. For instance, the work in [3] again proposes use of VGGNet based CNN for detection of pneumonia in children with an accuracy of 96.47%, which is very high and can be used for real-time purposes. This work can also use augmentation for improving its internal performance. ...
Air collection around the lung regions can cause lungs to collapse. Conditions like emphysema can cause chronic obstructive pulmonary disease (COPD), wherein lungs get progressively damaged, and the damage cannot be reversed by treatment. It is recommended that these conditions be detected early via highly complex image processing models applied to chest X-rays so that the patient’s life may be extended. Due to COPD, the bronchioles are narrowed and blocked with mucous, and causes destruction of alveolar geometry. These changes can be visually monitored via feature analysis using effective image classification models such as convolutional neural networks (CNN). CNNs have proven to possess more than 95% accuracy for detection of COPD conditions for static datasets. For consistent performance of CNNs, this paper presents an incremental learning mechanism that uses deep transfer learning for incrementally updating classification weights in the system. The proposed model is tested on 3 different lung X-ray datasets, and an accuracy of 99.95% is achieved for detection of COPD. In this paper, a model for temporal analysis of COPD detected imagery is proposed. This model uses Gated Recurrent Units (GRUs) for evaluating lifespan of patients with COPD. Analysis of lifespan can assist doctors and other medical practitioners to take recommended steps for aggressive treatment. A smaller dataset was available to perform temporal analysis of COPD values because patients are not advised continuous chest X-rays due to their long-term side effects, which resulted in an accuracy of 97% for lifespan analysis.
... It could give way to the identification of patients who are at high-risk of developing severe disease and mortality, and gauge the need for in-hospital admission and management. Previous work on the use of deep learning to identify pneumonia in chest X-rays showed 96% to 98% accuracy in pneumonia detection [6,7], and this strategy could be valuable for patients in remote areas where low-cost chest X-ray machines could be deployed. This may potentially lead to task shifting for community and other healthcare workers, who could instead focus on counselling and management of those requiring hospitalisation, resulting in curtailment in unnecessary referral and an overall reduction in healthcare costs. ...
... In a recent study, the Chagas et al [23] presented a realtime IoT system that will use CXIs to identify pneumonia where twelve different CNN models were used as extractors. The results have indicated that the optimum solution is the VGG19 framework. ...
SARS-CoV-2 has now spread to nearly every part
of the world, with the WHO declaring a pandemic because
of its rapid spread. One of the diagnostic procedures used to
detect the extent of the COVID-19 infection is Chest X-rays.
Chest X-rays are commonly used to diagnose lung disorders
in the beginning. To improve the accuracy of the computer-aided
diagnosis system, a research study assessed how well it
can correctly distinguish between non-COVID-19 pneumonia on
chest X-ray (CXR) images and COVID-19 pneumonia with the
alliance of Artificial Intelligence. COVID-19 pneumonia patients
(those that tested positive for COVID-19 antibodies) and non-
COVID-19 pneumonia patients (those who did not test positive
for COVID-19 antibodies) were included in the analysis. The
research was conducted using a standard dataset containing
1563 lung CT scan images of COVID-19 pneumonia and non-
COVID-19 pneumonia (virus) patient samples. The proposed
system has two Convolutional Neural Network (CNN) models.
The first CNN model using max-pooling operation achieved the
accuracy, precision, recall, and F1-Score of 98.22%, 98.81%,
99.33%, and 99.07% respectively and similarly, the second CNN
model using average pooling operation performed at 97.82%,
98.60%, 99.13%, and 98.86% respectively.
... The third theme of this issue covers "Intelligent real-time image processing and its extended fields". Chagas et al. [8] have designed twelve different CNN architectures, which are integrated with k-Nearest Neighbor (kNN), Naive Bayes, Random Forest, Multilayer Perceptron (MLP), and Support Vector Machine (SVM) for ImageNet dataset recognition. Their experimental results indicate the VGG19 model with the SVM classifier resulting as the best approach for pneumonia detection. ...
... The results showed that the VGG19 architecture with the SVM classifier using the radial basis function kernel was the best model to detect pneumonia from the chest radiographs. The suggested model achieved 96.47%, 96.46%, and 96.46% for accuracy, F1 score, and precision values, respectively (das Chagas et al., 2021). In another work conducted by Singh et al. (2021) a pipeline was developed that utilized CXIs to detect COVID-19 pneumonia. ...
X-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5-fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists.
