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Automated lung nodule detection in CT images by optimized CNN: impact of improved whale optimization algorithm

Kumar M. Kiran, Amalanathan Anthoniraj

Computer Assisted Methods in Engineering and Science

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2022

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Vol. 29, no. 1-2 spec.

7--31

EN

Lung cancer is one of the leading causes of cancer-related deaths among individuals.It should be diagnosed at the early stages, otherwise it may lead to fatality due to itsmalicious nature. Early detection of the disease is very significant for patients’ survival, andit is a challenging issue. Therefore, a new model including the following stages: (1) imagepre-processing, (2) segmentation, (3) proposed feature extraction and (4) classificationis proposed. Initially, pre-processing takes place, where the input image undergoes specificpre-processing. The pre-processed images are then subjected to segmentation, which iscarried out using the Otsu thresholding model. The third phase is feature extraction, wherethe major contribution is obtained. Specifically, 4D global local binary pattern (LBP)features are extracted. After their extracting, the features are subjected to classification,where the optimized convolutional neural network (CNN) model is exploited. For a moreprecise detection of a lung nodule, the filter size of a convolution layer, hidden unit inthe fully connected layer and the activation function in CNN are tuned optimally byan improved whale optimization algorithm (WOA) called the whale with tri-level enhancedencircling behavior (WTEEB) model.

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Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers

Fraiwan Luay, Hassanin Omnia, Fraiwan Mohammad, Khassawneh Basheer, Ibnian Ali M., Alkhodari Mohanad

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

1--14

EN

This paper investigates the application of different homogeneous ensemble learning methods to perform multi-class classification of respiratory diseases. The case sample involved a total of 215 subjects and consisted of 308 clinically acquired lung sound recordings and 1176 recordings obtained from the ICBHI Challenge database. These recordings corresponded to a wide range of conditions including healthy, asthma, pneumonia, heart failure, bronchiectasis or bronchitis, and chronic obstructive pulmonary disease. Feature representation of the lung sound signals was based on Shannon entropy, logarithmic energy entropy, and spectrogram-based spectral entropy. Decision trees and discriminant classifiers were employed as base learners to build bootstrap aggregation and adaptive boosting ensembles. The optimal structure of the investigated ensemble models was identified through Bayesian hyperparameter optimization and was then compared to typical classifiers in literature. Experimental results showed that boosted decision trees provided the best overall accuracy, sensitivity, specificity, F1-score, and Cohen's kappa coefficient of 98.27%, 95.28%, 98.9%, 93.61%, and 92.28%, respectively. Among the baseline methods, SVM provided the best yet a slightly poorer performance, as demonstrated by its average accuracy (98.20%), sensitivity (91.5%), and specificity (98.55%). Despite their simplicity, the investigated ensemble classification methods exhibited a promising performance for detecting a wide range of respiratory disease conditions. The data fusion approach provides a promising insight into an alternative and more suitable solution to reduce the effect of imbalanced data for clinical applications in general and respiratory sound analysis studies in specific.

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Machine learning in lung sound analysis: a systematic review

Palaniappan R., Sundaraj K., Ahamed N. U.

Biocybernetics and Biomedical Engineering

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2013

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Vol. 33, no. 3

129--135

EN

Machine learning has proven to be an effective technique in recent years and machine learning algorithms have been successfully used in a large number of applications. The development of computerized lung sound analysis has attracted many researchers in recent years, which has led to the implementation of machine learning algorithms for the diagnosis of lung sound. This paper highlights the importance of machine learning in computer-based lung sound analysis. Articles on computer-based lung sound analysis using machine learning techniques were identified through searches of electronic resources, such as the IEEE, Springer, Elsevier, PubMed and ACM digital library databases. A brief description of the types of lung sounds and their characteristics is provided. In this review, we examined specific lung sounds/disorders, the number of subjects, the signal processing and classification methods and the outcome of the analyses of lung sounds using machine learning methods that have been performed by previous researchers. A brief description on the previous works is thus included. In conclusion, the review provides recommendations for further improvements.