Reliable Learning with PDE-Based CNNs and DenseNets for Detecting COVID-19, Pneumonia, and Tuberculosis from Chest X-Ray Images

Marginean, Anca; Muntean, Delia; Muntean, George Adrian; Prişcu, A; Groza, Adrian; Slavescu, Radu Razvan; Timbus, Calin; Munteanu, Gabriel Zeno; Morosanu, Cezar Octavian; Cosnarovici, Maria Margareta; Pintea, Camelia-Mihaela

doi:10.3390/math9040434

Cited by 7 publications

(3 citation statements)

References 20 publications

(42 reference statements)

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“…The accuracy of the model is 0.964 on an average specificity and sensitivity of 91% showing that deep convolutional neural networks can be developed with high classification accuracy and can help in the diagnosis procedure. [25] proposed a Reliable Learning with Partial differential equation to detect various diseases from Chest X-rays.…”

Section: Literature Surveymentioning

confidence: 99%

Chest Diseases Prediction from X-ray Images using CNN Models: A Study

Mangeri¹,

S²,

Puppala³

et al. 2021

IJACSA

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Section: Literature Surveymentioning

confidence: 99%

Chest Diseases Prediction from X-ray Images using CNN Models: A Study

Mangeri¹,

S²,

Puppala³

et al. 2021

IJACSA

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“…For example, disease cases are usually much smaller than those without disease, or, as in the case of this paper, outpatient cases during toddler and post-puberty stages are very scarce, but these are a small number of cases, usually the system still must be able to distinguish them, so many existing studies are devoted to alleviating the problem of serious imbalances of X-ray data. The most commonly used methods are several: re-sampling the data by undersampling [9,10] or oversampling [11][12][13][14], using transfer learning [15][16][17] to pre-train network parameters with other datasets with relatively sufficient data, and using the target task dataset to fine-tune the model. In addition, the loss function of its CNNs is improved [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Data Imbalance Immunity Bone Age Assessment System Using Independent Autoencoders

Peng

Chan

2022

Applied Sciences

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Bone age assessment (BAA) is an important indicator of child maturity. Generally, a person is evaluated for bone age mostly during puberty stage; compared to toddlers and post-puberty stages, the data of bone age at puberty stage are much easier to obtain. As a result, the amount of bone age data collected at the toddler and post-puberty stages are often much fewer than the amount of bone age data collected at the puberty stage. This so-called data imbalance problem affects the prediction accuracy. To deal with this problem, in this paper, a data imbalance immunity bone age assessment (DIIBAA) system is proposed. It consists of two branches, the first branch consists of a CNN-based autoencoder and a CNN-based scoring network. This branch builds three autoencoders for the bone age data of toddlers, puberty, and post-puberty stages, respectively. Since the three types of autoencoders do not interfere with each other, there is no data imbalance problem in the first branch. After that, the outputs of the three autoencoders are input into the scoring network, and the autoencoder which produces the image with the highest score is regarded as the final prediction result. In the experiments, imbalanced training data with a positive and negative sample ratio of 1:2 are used, which has been alleviated compared to the original highly imbalanced data. In addition, since the scoring network converts the classification problem into an image quality scoring problem, it does not use the classification features of the image. Therefore, in the second branch, we also add the classification features to the DIIBAA system. At this time, DIIBAA considers both image quality features and classification features. Finally, the DenseNet169-based autoencoders are employed in the experiments, and the obtained evaluation accuracies are improved compared to the baseline network.

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“…Learning is one of the most efficient artificial intelligence capabilities; in [2], learning with PDE-based CNNs and dense nets for the purpose of detecting COVID-19, pneumonia, and tuberculosis from chest X-ray images was studied. In the same context, automatic COVID-19 detection from chest X-ray and CT-scan images was proposed [3] within a new meta-heuristic feature selection using an optimized convolutional neural network [4].…”

Section: Introductionmentioning

confidence: 99%

Sensitive Ant Algorithm for Edge Detection in Medical Images

2021

Self Cite

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Nowadays, reliable medical diagnostics from computed tomography (CT) and X-rays can be obtained by using a large number of image edge detection methods. One technique with a high potential to improve the edge detection of images is ant colony optimization (ACO). In order to increase both the quality and the stability of image edge detection, a vector called pheromone sensitivity level, PSL, was used within ACO. Each ant in the algorithm has one assigned element from PSL, representing the ant’s sensibility to the artificial pheromone. A matrix of artificial pheromone with the edge information of the image is built during the process. Demi-contractions in terms of the mathematical admissible perturbation are also used in order to obtain feasible results. In order to enhance the edge results, post-processing with the DeNoise convolutional neural network (DnCNN) was performed. When compared with Canny edge detection and similar techniques, the sensitive ACO model was found to obtain overall better results for the tested medical images; it outperformed the Canny edge detector by 37.76%.

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Reliable Learning with PDE-Based CNNs and DenseNets for Detecting COVID-19, Pneumonia, and Tuberculosis from Chest X-Ray Images

Cited by 7 publications

References 20 publications

Chest Diseases Prediction from X-ray Images using CNN Models: A Study

Chest Diseases Prediction from X-ray Images using CNN Models: A Study

Data Imbalance Immunity Bone Age Assessment System Using Independent Autoencoders

Sensitive Ant Algorithm for Edge Detection in Medical Images

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