Automated recognition of lung diseases in CT images based on the optimum-path forest classifier

Filho, Pedro Pedrosa Rebouças; Barros, Antônio Carlos Souza Albuquerque; Ramalho, Geraldo Luis Bezerra; Pereira, Clayton Reginaldo; Papa, João Paulo; Albuquerque, Victor Hugo C. de; Tavares, João Manuel R. S.

doi:10.1007/s00521-017-3048-y

Cited by 30 publications

(10 citation statements)

References 54 publications

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“…Based on heterogeneous pathologic images, medical image analysis has focused on disease prevention, prediction, detection, diagnosis, screening and so on. For example, in the early diagnose of chronic obstructive pulmonary disease, Filho et al [8] propose to utilize information from lung CT images to identify and classify lung diseases with the automatic feature extractor. Moreover, to detect the stages of cancer if affected, Sekaran et al [36] utilize CNN to predict the cancer images of the pancreas, which is embedded with the model of Gaussian mixture model with EM algorithm to identify the essential features from the CT Scan.…”

Section: Applications In Health Carementioning

confidence: 99%

Adaptive kernel selection network with attention constraint for surgical instrument classification

Hou

Zhang

Liu

et al. 2021

Neural Comput & Applic

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Computer vision (CV) technologies are assisting the health care industry in many respects, i.e., disease diagnosis. However, as a pivotal procedure before and after surgery, the inventory work of surgical instruments has not been researched with the CV-powered technologies. To reduce the risk and hazard of surgical tools’ loss, we propose a study of systematic surgical instrument classification and introduce a novel attention-based deep neural network called SKA-ResNet which is mainly composed of: (a) A feature extractor with selective kernel attention module to automatically adjust the receptive fields of neurons and enhance the learnt expression and (b) A multi-scale regularizer with KL-divergence as the constraint to exploit the relationships between feature maps. Our method is easily trained end-to-end in only one stage with few additional calculation burdens. Moreover, to facilitate our study, we create a new surgical instrument dataset called SID19 (with 19 kinds of surgical tools consisting of 3800 images) for the first time. Experimental results show the superiority of SKA-ResNet for the classification of surgical tools on SID19 when compared with state-of-the-art models. The classification accuracy of our method reaches up to 97.703%, which is well supportive for the inventory and recognition study of surgical tools. Also, our method can achieve state-of-the-art performance on four challenging fine-grained visual classification datasets.

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Section: Applications In Health Carementioning

confidence: 99%

Adaptive kernel selection network with attention constraint for surgical instrument classification

Hou

Zhang

Liu

et al. 2021

Neural Comput & Applic

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“…Many researchers have worked on the identification of chest issues [10][11][12][13][14][15]. Chest Xray image filtering is frequently utilized by radiologists to analyze numerous chest-related diseases in their underlying stages [16].…”

Section: Introductionmentioning

confidence: 99%

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

et al. 2021

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Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

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“…Numerous published works have been applied to improve the performance of CAD systems for pulmonary nodule analysis. In the case of insufficient resources and dataset, researchers usually use traditional machine learning methods such as multiple grey-level thresholding, linear discriminant analysis, distance transformation, and Support Vector Machine (SVM) for quick investigation on lung nodules [18]- [23]. However, using Deep Learning approaches in various medical imaging tasks has been a mainstream trend in the past ten years.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

Cao

et al. 2020

IEEE Access

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Lung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which contributes to mortality reduction. Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis. In this paper, a comprehensive review of the application and development of CAD systems is presented. The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics, and various medical competitions. We then introduce the main structure of a CAD system and present some efficient methodologies. Due to the extensive use of Convolutional Neural Network (CNN)based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods. Besides, we mainly select the CAD systems developed by state-of-theart CNNs with excellent performance and analyze their objectives, algorithms as well as results. Finally, research trends, existing challenges, and future directions in this field are discussed.

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Automated recognition of lung diseases in CT images based on the optimum-path forest classifier

Cited by 30 publications

References 54 publications

Adaptive kernel selection network with attention constraint for surgical instrument classification

Adaptive kernel selection network with attention constraint for surgical instrument classification

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

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