A<scp>cost‐sensitive</scp>deep learning‐based<scp>meta‐classifier</scp>for pediatric pneumonia classification using chest X‐rays

Ravi, Vinayakumar; Narasimhan, Harini; Pham, Tuan D.

doi:10.1111/exsy.12966

Cited by 17 publications

(4 citation statements)

References 40 publications

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“…It increases the prediction accuracy of machine learning models and produces explanations about the functioning of ML models, especially in deep learning and neural network models. CNN-based models have been implemented to determine and ascertain childhood pneumonia, using chest X-rays [54]. Deep-learning-based models help in CT scans, chest X-ray image analyses [55], heart disease prediction [56], and the reliable detection of lung diseases from medical images [57].…”

Section: Xai-based Classification Model For Predicting Cardiovascular...mentioning

confidence: 99%

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria¹,

Srinivasu

Ahmed

et al. 2022

Electronics

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Machine intelligence models are robust in classifying the datasets for data analytics and for predicting the insights that would assist in making clinical decisions. The models would assist in the disease prognosis and preliminary disease investigation, which is crucial for effective treatment. There is a massive demand for the interpretability and explainability of decision models in the present day. The models’ trustworthiness can be attained through deploying the ensemble classification models in the eXplainable Artificial Intelligence (XAI) framework. In the current study, the role of ensemble classifiers over the XAI framework for predicting heart disease from the cardiovascular datasets is carried out. There are 303 instances and 14 attributes in the cardiovascular dataset taken for the proposed work. The attribute characteristics in the dataset are categorical, integer, and real type and the associated task related to the dataset is classification. The classification techniques, such as the support vector machine (SVM), AdaBoost, K-nearest neighbor (KNN), bagging, logistic regression (LR), and naive Bayes, are considered for classification purposes. The experimental outcome of each of those algorithms is compared to each other and with the conventional way of implementing the classification models. The efficiency of the XAI-based classification models is reasonably fair, compared to the other state-of-the-art models, which are assessed using the various evaluation metrics, such as area under curve (AUC), receiver operating characteristic (ROC), sensitivity, specificity, and the F1-score. The performances of the XAI-driven SVM, LR, and naive Bayes are robust, with an accuracy of 89%, which is assumed to be reasonably fair, compared to the existing models.

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Section: Xai-based Classification Model For Predicting Cardiovascular...mentioning

confidence: 99%

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria¹,

Srinivasu

Ahmed

et al. 2022

Electronics

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“…In recent years, approaches from the field of deep learning have been increasingly applied to image processing (Ravi, Narasimhan, & Pham, 2022). Two instances of this are the extraction of picture features and the subsequent categorization of images based on those characteristics.…”

Section: Introductionmentioning

confidence: 99%

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN‐LSTM model

Arjun,

Kumar,

Dhanaraj

et al. 2024

Microscopy Res & Technique

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Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network‐Long Short‐Term Memory (RCNN‐LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN‐LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1‐score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes.Research Highlights Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.

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“…Consequently, designing a computer-aided diagnostic (CAD) system has become essential in supporting medical practitioners in establishing an accurate diagnosis of pneumonia on time [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images

et al. 2023

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The prompt and accurate identification of the causes of pneumonia is necessary to implement rapid treatment and preventative approaches, reduce the burden of infections, and develop more successful intervention strategies. There has been an increase in the number of new pneumonia cases and diseases known as acute respiratory distress syndrome (ARDS) as a direct consequence of the spread of COVID-19. Chest radiography has evolved to the point that it is now an indispensable diagnostic tool for COVID-19 infection pneumonia in hospitals. To fully exploit the technique, it is crucial to design a computer-aided diagnostic (CAD) system to assist doctors and other medical professionals in establishing an accurate and rapid diagnosis of pneumonia. This article presents a robust hybrid deep convolutional neural network (DCNN) for rapidly identifying three categories (normal, COVID-19 and pneumonia (viral or bacterial)) using X-ray image data sourced from the COVID-QU-Ex dataset. The proposed approach on the test set achieved a rate of 99.25% accuracy, 99.10% Kappa-score, 99.43% AUC, 99.24% F1-score, 99.25% recall, and 99.23% precision, respectively. The outcomes of the experiments demonstrate that the presented hybrid DCNN mechanism for identifying three categories utilising X-ray images is robust and effective.

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Acost‐sensitivedeep learning‐basedmeta‐classifierfor pediatric pneumonia classification using chest X‐rays

Cited by 17 publications

References 40 publications

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN‐LSTM model

A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images

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