Multi-Level Classification of Lung Pathologies in Neonates using Recurrence Features

Aujla, Sagarjit; Mohammed, Adel Gassab; Khan, Naimul; Umapathy, Karthikeyan

doi:10.1109/embc48229.2022.9871011

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…In addition, the proposed work performed pathological condition classification and included 3 clinical features which enabled us to separate certain lung conditions that are inseparable from images alone as verified by our clinical collaborators. To compare our previous initial work using recurrence features [11] with DTCWT features, we recomputed the results using recurrence features with updated datasets used in this work. We were able to achieve a per-image classification accuracy of 85.42% on the balanced dataset with LOO CV and 72.00% with LOO CV on the whole dataset using the recurrence features.…”

Section: Resultsmentioning

confidence: 99%

“…This section contains descriptions of the most important clinical markers used by clinicians in diagnosing different lung conditions as presented in our previous work [11]. These clinical markers are Pleural lines, A-lines, Separate B-lines, Coalescent B-lines and Consolidations.…”

Section: Lus Morphologies In Neonatesmentioning

confidence: 99%

“…In Bassiouny et al [10], 7 key LUS morphologies that are key markers for specific lung conditions were detected using a Faster Region-Based Convolutional Neural Network (FRCNN) object detection model. In our previous initial work [11], we proposed a feature extraction method that was designed to quantify the strong recurrence characteristics of the image morphologies used by doctors in diagnosing LUS images. We extracted scanlines from the images, these scanlines were shown to capture the commonly used LUS morphologies for diagnosis and used recurrence analysis to capture nonlinear features based on recurrence patterns captured in the scanlines.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform

Aujla¹,

Mohamed²,

Tan³

et al. 2023

Preprint

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Annually 8500 neonatal deaths are reported in the US due to respiratory failure, this is a third of all neonatal deaths in the US. Recently, Lung Ultrasound (LUS), due to its ionizing-radiation free nature, portability, and being cheaper is gaining wide acceptability as a diagnostic tool for different lung conditions. However, lack of highly trained medical professionals has limited its use especially in remote areas. To address this, an automated screening system that captures characteristics of the LUS patterns can be of significant assistance to clinicians who are not experts in lung ultrasound (LUS) images. In this paper, we propose a feature extraction method designed to quantify the spatially-localized line patterns and texture patterns found in LUS images. Using the dual-tree complex wavelet transform (DTCWT) and four types of common image features we propose a method to classify the LUS images into 6 common neonatal lung conditions. These conditions are normal lung, pneumothorax (PTX), transient tachypnea of the newborn (TTN), respiratory distress syndrome (RDS), chronic lung disease (CLD) and consolidation (CON) that could be pneumonia or atelectasis. The proposed method using DTCWT decomposition extracted global statistical, grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and linear binary pattern (LBP) features to be fed to a linear discriminative analysis (LDA) based classifier. Using 15 best DTCWT features along with 3 clinical features the proposed approach achieved a per-image classification accuracy of 92.78% with a balanced dataset containing 720 images from 24 patients and 74.39% with the larger (class unbalanced) dataset containing 1550 images from 42 patients. Likewise, the proposed method achieved a maximum per-subject classification accuracy of 81.53% with 43 DTCWT features and 3 clinical features using the balanced dataset and 64.97% with 13 DTCWT features and 3 clinical features using the unbalanced larger dataset.

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Section: Resultsmentioning

confidence: 99%

Section: Lus Morphologies In Neonatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform

Aujla¹,

Mohamed²,

Tan³

et al. 2023

Preprint

View full text Add to dashboard Cite

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Classification of lung pathologies in neonates using dual-tree complex wavelet transform

Aujla,

Mohamed,

Tan

et al. 2023

BioMed Eng OnLine

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Introduction Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis. Methods We propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class. Results Using DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%. Conclusion Our work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.

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Multi-Level Classification of Lung Pathologies in Neonates using Recurrence Features

Cited by 2 publications

References 17 publications

Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform

Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform

Classification of lung pathologies in neonates using dual-tree complex wavelet transform

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