Semi-supervised labelling of chest x-ray images using  unsupervised clustering for ground-truth generation

Agughasi, Victor Ikechukwu; Srinivasiah, Murali

doi:10.31763/aet.v2i3.1143

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…Following pre-processing, the ResNet-based model was trained using transfer learning. Using ImageNet weights as a backbone, the model took advantage of ResNet's robust feature extraction [29] capabilities and adapted them to the specific task of COPD diagnosis [34]. This method permits faster convergence and performs better than models trained from scratch.…”

Section: Pre-processing Techniquesmentioning

confidence: 99%

Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

Agughasi

2024

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Chronic Obstructive Pulmonary Disease (COPD) is a predominant global health concern, ranking third in mortality rates, yet frequently remains undiagnosed until its advanced stages. Given its prevalence, the need for innovative and widely accessible diagnostic tools has never been more paramount. While spirometry tests serve as conventional diagnostic benchmarks, their reach remains limited, especially in regions with constrained medical resources. The presented research harnesses deep learning algorithms to facilitate early-stage COPD detection, specifically targeting Chest X-rays (CXRs). The clinically annotated VinDR-CXR dataset provides the primary foundation for model training, complemented by incorporating the ChestX-ray14 dataset for initial model pre-training. Such a dual-dataset strategy augments model generalization and adaptability. Among several explored Convolutional Neural Network (CNN) architectures, the Xception model emerges as a frontrunner. Through transfer learning methodologies, this model produces a noteworthy recall rate of 98.2%, markedly surpassing the metrics of the ResNet50 model. Recognizing the imperative for transparency in AI applications in medical imaging, the research integrates essential explainability approaches viz: Gradient Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). These techniques elucidate the AI’s decision-making process, offering invaluable visual and analytical insights for fostering trust among medical professionals. In essence, this study not only underscores the potential of integrating AI with medical imaging for COPD detection but also accentuates the pivotal role of transparency in AI-driven medical interventions.

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Section: Pre-processing Techniquesmentioning

confidence: 99%

Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

Agughasi

2024

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“…Nonetheless, to ensure the model's resilience and adaptability, specific preprocessing steps were undertaken. This involved rescaling and normalizing the images [15]. During training, data augmentation became crucial.…”

Section: Dataset Acquisition and Preprocessingmentioning

confidence: 99%

The Superiority of Fine-tuning over Full-training for the Efficient Diagnosis of COPD from CXR Images

Agughasi

2024

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This research investigates the use of deep learning for diagnosing lung diseases like Chronic Obstructive Pulmonary Disease (COPD) using Chest X-rays (CXR). The study compares the impact of deep learning on improving these diagnoses by comparing the performance of models trained from the scratch with those enhanced through fine-tuning established architectures like InceptionV3, ResNet50, and VGG-19. The study revealed that fine-tuning pre-trained models offers significant benefits: faster convergence, improved stability, and increased accuracy. Data augmentation techniques were found to be particularly useful when dealing with limited or unbalanced datasets. A custom CNN model, Iyke-Net, showed promising results when fine-tuned. Interestingly, it was observed that models using grayscale images outperformed those using colour images in disease classification, suggesting that colour information might be less critical than previously thought for certain diagnostic procedures. The study emphasizes the importance of balancing model complexity with computational efficiency and diagnostic accuracy. It advocates for refining existing deep learning models for COPD diagnosis from CXR images, paving the way for further innovations in AI-enhanced medical diagnostics.

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“…The use of bounding boxes has been abandoned in favor of image segmentation, giving rise to various scene text detectors in recent years [9]- [13]. SSC-Net, inspired by [14] approach to segmentation, which links all picture elements in the same instance.…”

Section: Introductionmentioning

confidence: 99%

CRNN model for text detection and classification from natural scenes

Prakash,

Yeliyur Hanumanthaiah,

Bannur Mayigowda

2024

IJ-AI

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<span lang="EN-US">In the emerging field of computer vision, text recognition in natural settings remains a significant challenge due to variables like font, text size, and background complexity. This study introduces a method focusing on the automatic detection and classification of cursive text in multiple languages: English, Hindi, Tamil, and Kannada using a deep convolutional recurrent neural network (CRNN). The architecture combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks for effective spatial and temporal learning. We employed pre-trained CNN models like VGG-16 and ResNet-18 for feature extraction and evaluated their performance. The method outperformed existing techniques, achieving an accuracy of 95.0%, 96.3%, and 96.2% on ICDAR 2015, ICDAR 2017, and a custom dataset (PDT2023), respectively. The findings not only push the boundaries of text detection technology but also offer promising prospects for practical applications.</span>

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Semi-supervised labelling of chest x-ray images using unsupervised clustering for ground-truth generation

Cited by 8 publications

References 27 publications

Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

The Superiority of Fine-tuning over Full-training for the Efficient Diagnosis of COPD from CXR Images

CRNN model for text detection and classification from natural scenes

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