Accurate automatic localisation of lung nodules using Graph Cut and snakes algorithms

Negar, Mirderikvand; Naderan, Marjan; Jamshidnezhad, Amir

doi:10.1109/iccke.2016.7802139

Cited by 6 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each pixel is giving a binary code based on its value relative to its neighboring pixels. This is done by giving a value of 1 neighboring pixel that have values greater than or equal to the value of the pixel being considered and a value of zero to those with lower values [8][9]. This results in an n-bit binary code describing each pixel, where n is the number of neighbors.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The approach generates a classifier to combine the detections of the Classifier on all the slices for a single patient. All the nodule regions detected for a patient with the regions that are redder indicating more detections at those pixel locations [9]. There are false positive detections in various places of the scan, but only a few regions have multiple detections i.e.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The only preprocessing involved in this approach is that we normalize each slice and truncate the volumes or pad them so that they have the same shape (since the number of slices varies from patient to patient). The approach, we tried is like the pre-determined approaches with a machine learning, except that the classification method to reduce false positives is substituted for the heuristic method described in the approach [9]. As with the second approach, during training, patches are labeled cancerous if they come from a cancer patient, and non-cancerous otherwise.…”

Section: Dataset Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

Ahmed¹,

Al_Barazanchi²,

Mhana³

et al. 2019

PEN

View full text Add to dashboard Cite

These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms.

show abstract

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

Ahmed¹,

Al_Barazanchi²,

Mhana³

et al. 2019

PEN

View full text Add to dashboard Cite

show abstract

“…Apple iTunes and Google Play stores offer many applications for mobile health. 10 Intelligent methods as the learning-based interactive models have been introduced in medical fields such as stroke imaging, 11,12 medical imaging, 13 medical decision support, 14 and medical data analysis. 15 Patients with stroke can receive medical care information by the interactive models, which provide them with the prognosis and required treatments.…”

Section: Introductionmentioning

confidence: 99%

Consideration of Intelligent Applications to Support Diabetic Patients: A Scoping Review for Nutrition Mobile Phone Apps

Jamshidnezhad

Koohmareh

Pazahr

2020

J. Contemp. Med. Sci.

View full text Add to dashboard Cite

Introduction: Today, design and development of smartphone applications is growing, rapidly. Health care applications have developed as a means of teaching, health promotion, patients’ self-care and controlling the nutrition of diabetic patients. Objectives: Identify the functionality of smart applications in the mobile platform for controlling, teaching and healthy lifestyles in the field of nutrition for diabetic patients. Method: Current review was conducted on the mobile platform to consider the functionalities of available applications to support the diabetic patients with the intelligent tools. The results of inclusion criteria (such as nutrition, weight control, blood pressure, education and interactive analysis tools) were evaluated and organized in the form of a scoping review. All applications in the systematic search were independent of both operating systems. The search samples were found in May 2018 by experts of medical informatics. Findings: 273 potential applications were identified according to the results of the inclusion criteria with evidence based strategies and systematic evaluations to find the apps for diabetic patients. Approximately 29% of apps in both stores (79/273) met primary assessment criteria. Of these, 40 % of apps (32/79) excluded of more consideration due to lack in the most criteria of this study. The remaining apps are described as follows: 8% of total apps (22/273) and 4% of the apps (11/273) met general criteria and all criteria (including general and special) in the Google play store, respectively. Only 2 apps met general criteria with the Apple iOS while none of them covered with special criteria according to the objectives of this study. Conclusion: There are various applications for control, education, and self-care in diabetes. Google is an acceptable platform for developers interested in examining interactive apps and techniques in disease control, education and healthy lifestyle programs.

show abstract

“…Within the medical industry such as radiology, pathology, and dermatology, image analysis has been successfully achieved by AI at exceeding speeds with unparalleled accuracy. Though the AI’s diagnostic confidence is never 100%, the collaboration between machines and physicians increase the reliability of system performance ( 18 ). Medical administration has been transformed as AI are able to recognise natural language processing to identify the rapidly growing scientific literature and aggregate years of electronic medical records.…”

Section: Introductionmentioning

confidence: 99%

Top Mobile Applications in Pediatrics and Children’s Health: Assessment and Intelligent Analysis Tools for a Systematic Investigation

Smhm

Jamshidnezhad

2019

MJMS

Self Cite

View full text Add to dashboard Cite

The development of intelligent software in recent years has grown rapidly. Mobile health has become a field of interest as a tool for childcare, especially as a means for parents of children with diverse diseases and a resource to promote their health conditions. Current systematic review was conducted to survey the functionalities of available applications on the mobile platform to support pediatrics intelligent diagnosis and children healthcare. Results which met the inclusion criteria (such as patient monitoring, decision support, diagnosis support) were obtained, assessed and organised into a checklist. In this study, 379 potential apps were identified using the search feature in Apple App Store and Google Play Store. After careful consideration of the selected apps, only three (Google Play Store) and one (iTunes Store), fulfilled all the general inclusion criteria and special criteria, such as intelligence tools. The results showed that Artificial Intelligence (AI) was used minimally in diagnostic apps due to a limited amount of mobile hardware and software, such as the reliable programming of intelligent algorithms.

show abstract

Accurate automatic localisation of lung nodules using Graph Cut and snakes algorithms

Cited by 6 publications

References 14 publications

Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

Consideration of Intelligent Applications to Support Diabetic Patients: A Scoping Review for Nutrition Mobile Phone Apps

Top Mobile Applications in Pediatrics and Children’s Health: Assessment and Intelligent Analysis Tools for a Systematic Investigation

Contact Info

Product

Resources

About