High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images

Shamrat, F. M. Javed Mehedi; Azam, Sami; Karim, Asif; Ahmed, Kawsar; Bui, Francis M.; Boer, Friso De

doi:10.1016/j.compbiomed.2023.106646

Cited by 86 publications

(32 citation statements)

References 46 publications

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“…In Yoo et al (2020), assess the performance of ResNet-34-based nodule prediction algorith on CXRs. Shamrat et al fine-tune a MobileLungNetV2 model on the pre-processed images by CLAHE and Gaussian Filter (Shamrat et al 2023). These works only use existing deep learning models, and do not design a dedicated framework according to the characteristics of pulmonary nodules.…”

Section: Methods For Nodule Detectionmentioning

confidence: 99%

“…Many researchers have made great efforts on pulmonary nodule detection in x-ray images (Nam et al 2019, Majkowska et al 2020, Yoo et al 2020, Shamrat et al 2023. Some researchers have focused on coarse-grained classification of x-ray images (Guan et al 2020, Lee et al 2022, while others have pursued finegrained pulmonary nodule detection, aiming to provide precise nodule locations denoted by bounding boxes (Pesce et al 2019, Li et al 2020a, Tsai and Peng 2022.…”

Section: Introductionmentioning

confidence: 99%

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Pulmonary nodule detection in x-ray images by feature augmentation and context aggregation

Liu,

Wu,

Wang

et al. 2024

Phys. Med. Biol.

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Recent developments in X-ray image based pulmonary nodule detection have achieved remarkable results. However, existing methods are focused on transferring off-the-shelf coarse-grained classification models and fine-grained detection models rather than developing a dedicated framework optimized for nodule detection. In this paper, we propose PN-DetX, which as we know is the first dedicated pulmonary nodule detection framework. PN-DetX incorporates feature fusion and self-attention into X-ray based pulmonary nodule detection tasks, achieving improved detection performance. Specifically, PN-DetX adopts CSPDarknet backbone to extract features, and utilizes feature augmentation module to fuse features from different levels followed by context aggregation module to aggregate semantic information. To evaluate the efficacy of our method, we collect a LArge-scale Pulmonary NOdule Detection dataset, LAPNOD, comprising 2,954 X-ray images along with expert-annotated ground truths. As we know, this is the first large-scale chest X-ray pulmonary nodule detection dataset. Experiments demonstrates that our method outperforms baseline by 3.8% mAP and 5.1% AP0.5. The generality of our approach is also evaluated on other publicly available dataset. We aspire for our method to serve as an inspiration for future research in the field of pulmonary nodule detection. The data and codes will be made in public.

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Section: Methods For Nodule Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pulmonary nodule detection in x-ray images by feature augmentation and context aggregation

Liu,

Wu,

Wang

et al. 2024

Phys. Med. Biol.

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“…The selection of Hyper-parameters (HP) is vital to make the deep learning framework more powerful. 20 Given the large variety of options for all hyper-parameters, we tested the effectiveness of the CX-RaysNet architecture by utilizing several settings for the hyper-parameters to find the best value for each one. The final hyper-parameter values are displayed in Table 2.…”

Section: Hyper-parametersmentioning

confidence: 99%

“…15 AI-based deep learning approaches outperform and enhance traditional diagnostic methods by enabling automated analysis of medical images with high accuracy and efficiency. 16 Recent studies have explored the application of pre-trained CNN models, [17][18][19] transfer learning, [20][21][22][23][24] and extreme/ensemble learning [25][26][27] techniques to improve the performance of multi-class Lung disease detection from chest X-rays. Transfer learning allows pre-trained models to be fine-tuned on smaller medical image datasets that enable CNN to perform efficient knowledge transfer and faster convergence during training.…”

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confidence: 99%

Detection of multi‐class lung diseases based on customized neural network

Ali,

Wang,

Shi

2024

Computational Intelligence

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In the medical image processing domain, deep learning methodologies have outstanding performance for disease classification using digital images such as X‐rays, magnetic resonance imaging (MRI), and computerized tomography (CT). However, accurate diagnosis of disease by medical personnel can be challenging in certain cases, such as the complexity of interpretation and non‐availability of expert personnel, difficulty at pixel‐level analysis, etc. Computer‐aided diagnostic (CAD) systems with proper training have shown the potential to enhance diagnostic accuracy and efficiency. With the exponential growth of medical data, CAD systems can analyze and extract valuable information by assisting medical personnel during the disease diagnostic process. To overcome these challenges, this research introduces CX‐RaysNet, a novel deep‐learning framework designed for the automatic identification of various lung disease classes in digital chest X‐ray images. The core novelty of the CX‐RaysNet framework lies in the integration of both convolutional and group convolutional layers, along with the usage of small filter sizes and the incorporation of dropout regularization. This phenomenon helps us optimize the model's ability to distinguish minute features that reveal different lung diseases. Additionally, data augmentation techniques are implemented to augment the training and testing datasets, which enhances the model's robustness and generalizability. The performance evaluation of CX‐RaysNet reveals promising results, with the proposed model achieving a multi‐class classification accuracy of 97.25%. Particularly, this study represents the first attempt to optimize a model specifically for low‐power embedded devices, aiming to improve the accuracy of disease detection while minimizing computational resources.

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“…Indeed, various optimization techniques have shown different performances in training CNN models [21,22,[24][25][26][27][28][29]. For example, an Xception pre-trained model to classify the chest X-rays from normal, COVID-19, and pneumonia has shown the best accuracy with the optimizer Root Mean Square Propagation (RMSProp) [26], but the other studies using CNN models showed the best accuracy with Adaptive Moment Estimation (Adam) optimizer [24,29] or Adaptive Gradient [25]. These results suggest that the performance of the optimizers is dependent on the training models.…”

Section: Introductionmentioning

confidence: 99%

Optimization of vision transformer-based detection of COVID-19 from chest X-ray images

Ko,

Park,

Woo

2023

Preprint

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Background: For diagnosing coronavirus disease 2019 (COVID-19), chest X-rays have emerged as a preferred modality because of their accessibility, affordability, and capability to identify various pathologies. Recent advances in deep learning algorithms have shown promise in distinguishing COVID-19 from other lung diseases. However, the use of different optimization methods can affect the performance of the deep learning models. We aimed to compare the effects of the different optimization methods, identifying the best-performing algorithms for the detection of COVID-19 using chest X-rays. Methods: Chest X-ray images, including the seven classes of Normal, COVID-19, Viral Pneumonia, Bacterial Pneumonia, Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and Tuberculosis, were obtained. We trained the Vision Transformer (ViT) model using different optimizers such as Adaptive Moment Estimation (Adam), AdamW, Nesterov accelerated Adam (NAdam), Rectified Adam (RAdam), Stochastic Gradient Descent with weight decay (SGDW), and Momentum, and compared their performances. Results: We found that the RAdam optimizer at a learning rate of 10-5 achieved the highest accuracy, highest weighted average of F1-score, and lowest false negative rate of COVID-19 for both 4 Class and 7 Class Dataset. On the other hand, AdamW showed better performance for the samples with small sample sizes. The optimizers derived from Adam (i.e. Adam, AdamW, NAdam, and RAdam), showed robust results against different learning rates, while SGDW and Momentum showed less significant robustness. Conclusions: We suggest that Adam-derived optimizers, particularly RAdam, showed best performance in training the ViT model for detecting COVID-19 using chest X-ray images. Our results may help in the efforts to improve the performance of the model and to make it clinically useful.

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High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images

Cited by 86 publications

References 46 publications

Pulmonary nodule detection in x-ray images by feature augmentation and context aggregation

Pulmonary nodule detection in x-ray images by feature augmentation and context aggregation

Detection of multi‐class lung diseases based on customized neural network

Optimization of vision transformer-based detection of COVID-19 from chest X-ray images

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