Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images

Bhandary, Abhir; Prabhu, G. Ananth; Rajinikanth, V.; Krishnan, Palani Thanaraj; Satapathy, Suresh Chandra; Robbins, David E.; Shasky, Charles; Zhang, Yudong; Tavares, João Manuel R. S.; Raja, N. Sri Madhava

doi:10.1016/j.patrec.2019.11.013

Cited by 293 publications

(144 citation statements)

References 32 publications

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“…The obtained results depict that the proposed Covid-ResNes gave good identification accuracy of 96.23% compared to Covid-Net 83.5%. Bhandary et al (2020) reported a deep learning framework to classify lung abnormalities like pneumonia using chest X-ray images and cancer using lung CT images. The proposed model was based on a Modified AlexNet model (MAN).…”

Section: Related Workmentioning

confidence: 99%

“…The performance of the proposed classification model was evaluated based on accuracy, sensitivity, specificity, precision, and F1 score (Bhandary et al, 2020;Blum & Chawla, 2001). Given the number of false positives (FP), true positives (TP), false negatives (FN) and true negatives (TN), the parameters are mathematically defined as follows:…”

Section: Performance Metricsmentioning

confidence: 99%

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Using X-ray images and deep learning for automated detection of coronavirus disease

Asnaoui

Youness

2020

Journal of Biomolecular Structure and Dynamics

259

216

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Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201). ARTICLE HISTORY

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Section: Related Workmentioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%

Using X-ray images and deep learning for automated detection of coronavirus disease

Asnaoui

Youness

2020

Journal of Biomolecular Structure and Dynamics

259

216

View full text Add to dashboard Cite

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“…With the development of digital technology, image-based diagnosis techniques have been widely used to help doctors investigate problems with organs that are underneath the skin and/or deep inside the human body [1][2][3][4][5][6][7][8][9][10][11]. For example, doctors have used X-ray imaging to capture lung and/or bone images that can help to indicate whether a disease/injury exists in these organs [9,10]. To diagnose issues with the human brain, the Computer-Tomography (CT) and/or Magnetic Resonance Imaging (MRI) techniques have been widely used [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…However, the use of captured images is still dependent on personal knowledge and experiences of doctors. To overcome this problem, Computer-Aided Diagnosis systems (CAD) have been developed to assist doctors in the diagnosis and treatment processes [1][2][3][4][5][6][7][8][9][10]. As indicated by its name, the CAD systems can serve as an additional expert in the double screening process that aims to enhance the human diagnostic performance based on a computer program [11].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Nguyen

Kang

Pham

et al. 2020

Sensors

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Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

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“…The premature detection of pneumonia and the possible treatment implementation will reduce the death rate in humans [2,3]. The lung infection due to pneumonia is normally assessed using the imaging procedures, such as the Computed-Tomography (CT) and Chest Radiographs (X-ray).…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm

Satapathy

Hemanth

Kadry

et al. 2020

Preprint

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Infection/disease in lung is one of the acute illnesses in humans. Pneumonia is one of the major lung diseases and each year; the death rate due to the untreated pneumonia is on rise globally. From December 2019; the pneumonia caused by the Coronavirus Disease (COVID-19) has emerged as a global threat due to its rapidity. The clinical level assessment of the COVID-19 is normally performed with the Computed-Tomography scan Slice (CTS) or the Chest X-ray. This research aims to propose an image processing system to examine the COVID-19 infection in CTS. This work implements Cuckoo-Search-Algorithm (CSA) monitored Kapur/Otsu image thresholding and a chosen image segmentation procedure to extract the pneumonia infection. After extracting the COVID-19 infection from the CTS, a relative assessment is then executed with the Ground-Truth-Image (GTI) offered by a radiologist and the essential performance measures are then computed to confirm the superiority of the proposed technique. This work also presents a comparative assessment among the segmentation procedures, such as Level-Set (LS) and Chan-Vese (CV) methods. The experimental outcome authenticates that, the results by Kapur and Otsu threshold are approximately similar when the LS is implemented and the CV with the Otsu presents better values of Jaccard, Dice and Accuracy compared to other methods presented in this research.

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Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images

Cited by 293 publications

References 32 publications

Using X-ray images and deep learning for automated detection of coronavirus disease

Using X-ray images and deep learning for automated detection of coronavirus disease

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm

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