DWT-LBP Descriptors for Chest X-Ray View Classification

Pardeshi, Rajmohan; Patil, R. R.; Ansingkar, Nirupama; Deshmukh, Prapti D.; Biradar, Somnath

doi:10.1007/978-981-15-1518-7_32

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The feature extraction unit involves a process of extracting textual and frequency domain features (widely used in image processing works [40] , [41] , [42] ). The texture feature set is generated using CXR image in spatial domain, gray-level co-occurrence matrix (GLCM) [43] , [44] and gray-level difference matrix (GLDM) [4] , [45] .…”

Section: Methodsmentioning

confidence: 99%

Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

Rajpal

Lakhyani²,

Singh

et al. 2021

Chaos, Solitons & Fractals

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Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued. As pneumonia and COVID-19 exhibit similar/ overlapping symptoms and affect the human lungs, a distinction between the chest X-ray images of pneumonia patients and COVID-19 patients becomes challenging. In this work, we have modeled the COVID-19 classification problem as a multiclass classification problem involving three classes, namely COVID-19, pneumonia, and normal. We have proposed a novel classification framework which combines a set of handpicked features with those obtained from a deep convolutional neural network. The proposed framework comprises of three modules. In the first module, we exploit the strength of transfer learning using ResNet-50 for training the network on a set of preprocessed images and obtain a vector of 2048 features. In the second module, we construct a pool of frequency and texture based 252 handpicked features that are further reduced to a set of 64 features using PCA. Subsequently, these are passed to a feed forward neural network to obtain a set of 16 features. The third module concatenates the features obtained from first and second modules, and passes them to a dense layer followed by the softmax layer to yield the desired classification model. We have used chest X-ray images of COVID-19 patients from four independent publicly available repositories, in addition to images from the Mendeley and Kaggle Chest X-Ray Datasets for pneumonia and normal cases. To establish the efficacy of the proposed model, 10-fold cross-validation is carried out. The model generated an overall classification accuracy of 0.974 0.02 and a sensitivity of 0.987 0.05, 0.963 0.05, and 0.973 0.04 at 95% confidence interval for COVID-19, normal, and pneumonia classes, respectively. To ensure the effectiveness of the proposed model, it was validated using an independent Chest X-ray cohort and an overall classification accuracy of 0.979 was achieved. Comparison of the proposed framework with state-of-the-art methods reveal that the proposed framework outperforms others in terms of accuracy and sensitivity. Since interpretability of results is crucial in the medical domain, the gradient-based localizations are captured using Gradient-weighted Class Activation Mapping (Grad-CAM). In su...

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Section: Methodsmentioning

confidence: 99%

Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

Rajpal

Lakhyani²,

Singh

et al. 2021

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

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“…Several approaches have been employed to enhance medical image quality, particularly in chest X-ray data. A histogram equalization-based model has shown good result in enhancing chest X-ray data, providing better visual results for interpretability and noise reduction [36,37,38]. The implementation of enhancement method for specific cases was also done such as to boost deep learning model for classification tasks in Covid-19 data using histogram equalization-based method [39,40,41] [44], the implementation of EFF was also carried out for the same cases by Setiawan [45].…”

Section: Related Workmentioning

confidence: 99%

Automatic Radiology Report Generator Using Transformer With Contrast-Based Image Enhancement

Tsaniya,

Fatichah,

Suciati

2024

IEEE Access

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Writing radiology reports based on radiographic images is a time-consuming task that demands the expertise of skilled radiologists. Consequently, the integration of technology capable of automated report generation would be advantageous. Developing a coherent predictive text is the main challenge in automatic report generation. It is necessary to develop methods that can increase the relevance of features in producing predictive text. This study constructed a medical report generator model using the transformer approach and image enhancement implementation. To leverage the visual and semantic features, an approach to enhance the noise-prone nature of the medical image is explored in this study along with the transformers method to generate a radiology report based on Chest X-ray images. Four contrast-based image enhancement methods were used to investigate the effect of image enhancement techniques on the radiology report generator. The encoder-decoder model is used with text feature embedding using Bidirectional Encoder Representation from Transformer (BERT) and visual feature extraction utilizing a pre-trained model ChexNet and Multi-Head Attention (MHA) mechanism. The performance of the MHA model with gamma correction is 5% in better with a 0.377 value using the Bilingual Assessment Understudy (BLEU) with 4 n-gram evaluation. MHA also produces 15% better results with a 0.412 value than the baseline model. This method is able to outperform the baseline model and other previous works. It can be concluded that the use of transformer MHA encoder layer and BERT is effective in leveraging visual and text features. Additionally, the inclusion of an image enhancement approach has been found to have a positive impact on the model's performance.

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DWT-LBP Descriptors for Chest X-Ray View Classification

Cited by 2 publications

References 12 publications

Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

Automatic Radiology Report Generator Using Transformer With Contrast-Based Image Enhancement

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