5K+ CT Images on Fractured Limbs: A Dataset for Medical Imaging Research

Ruikar, Darshan D.; Santosh, K. C.; Hegadi, Ravindra S.; Rupnar, Lakhan; Choudhary, Vivek Anant

doi:10.1007/s10916-021-01724-9

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…This system applies to a variety of different types of lung nodes. The workflow is with deep learning classifiers with the classification of COVID-19 screening of the pulmonary CT scan infection [ 20 ]. The author modified the CT scans to segmented images with the support of CNN architecture [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…Authors worked on the CT scans of lungs and applied deep learning strategies like CNN and U-NET for the image enhancements and successfully segmented the images. From the studies [ 20 – 23 ], we can observe significant drawbacks that the segmentation of the images was not performed with better dice coefficient due to heavyweight architectures and higher resolution of the CT scans. We tried to outperform this in our proposed DB-NET.…”

Section: Introductionmentioning

confidence: 99%

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A bi-directional deep learning architecture for lung nodule semantic segmentation

et al. 2022

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Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A bi-directional deep learning architecture for lung nodule semantic segmentation

et al. 2022

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“…At present, a topic that is becoming highly relevant in the application of deep learning (AI) in health are the so-called computer-aided detection (CADe) and diagnosis (CADx) in which, through a system based on recognition of patterns in images, meaning lesions in complex structures can be identified and classified through the different shapes and intensity levels in the pixels; some examples of this are the CADe/CADx systems developed for detection of lung and breast cancer, colonoscopy, etc. [5][6][7][8], but there are still no applications thus far for the classification and diagnosis of cardiac arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification

et al. 2021

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Arrhythmias are the most common events tracked by a physician. The need for continuous monitoring of such events in the ECG has opened the opportunity for automatic detection. Intra- and inter-patient paradigms are the two approaches currently followed by the scientific community. The intra-patient approach seems to resolve the problem with a high classification percentage but requires a physician to label key samples. The inter-patient makes use of historic data of different patients to build a general classifier, but the inherent variability in the ECG’s signal among patients leads to lower classification percentages compared to the intra-patient approach. In this work, we propose a new unsupervised algorithm that adapts to every patient using the heart rate and morphological features of the ECG beats to classify beats between supraventricular origin and ventricular origin. The results of our work in terms of F-score are 0.88, 0.89, and 0.93 for the ventricular origin beats for three popular ECG databases, and around 0.99 for the supraventricular origin for the same databases, comparable to supervised approaches presented in other works. This paper presents a new path to make use of ECG data to classify heartbeats without the assistance of a physician despite the needed improvements.

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AI Solutions to Public Health Issues

Santosh

Gaur

2021

Artificial Intelligence and Machine Learning in Public Healthcare

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5K+ CT Images on Fractured Limbs: A Dataset for Medical Imaging Research

Cited by 9 publications

References 28 publications

A bi-directional deep learning architecture for lung nodule semantic segmentation

A bi-directional deep learning architecture for lung nodule semantic segmentation

A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification

AI Solutions to Public Health Issues

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