Retrieval with Clustering in a Case-Based Reasoning System for Radiotherapy Treatment Planning

Khussainova, Gulmira; Petrović, Sanja; Jagannathan, Rupa

doi:10.1088/1742-6596/616/1/012013

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Leave-one-out is an evaluation method to measure the performance of an algorithm. It is a cross validation approach where training is performed on all data except for one point and prediction is made for that point [17]. The matching cases to the new case can be found by using K-NN method.…”

Section: Resultsmentioning

confidence: 99%

“…P. Singh presented a knowledge support system based on CBR which improves the quality of life of asthmatic patients [16]. G. Khussainova et al introduced a CBR system to provide radiotherapy treatment plan for patients suffer from brain cancer and to revise the retrieval mechanism by employing well-known clustering methods which have more success rate as compare to the original system [17]. R. M. Saraiva et al presented hybrid CBR and RBR approach to support cancer diagnosis [18].…”

Section: Cbr In Medicinementioning

confidence: 99%

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Impact of Genetic Optimization on the Prediction Performance of Case-Based Reasoning Algorithm in Liver Disease

Takkar¹

2017

IJPE

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Liver illness is the most hazardous ailment that influences a large number of individuals consistently and ends man's life. An effective diagnosis model is required in the process of liver disease treatment. This study accordingly aims to employ Case-Based Reasoning (CBR) methodology supported by Genetic Algorithm (GA) to optimize the prediction results of liver disease and to analyze their performances on different datasets. CBR methodology has been implemented to find the prediction results of liver disease for different datasets. We proposed a GA-based CBR framework to compare its performance with CBR in order to observe how effective it is at predicting liver illness. CBR prediction accuracy is very low so it is not very much appreciated. The proposed GA-CBR integrated model outperforms the CBR model by achieving better accuracy for all used datasets of liver disease. In this optimization of weights of features and selection of suitable instances are done simultaneously rather than separately. This leads to better prediction performance as compare to independent models. The outcome of this model illustrates that performance of usual CBR enhances fundamentally by utilizing our integrated GA-CBR model approach.

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Section: Resultsmentioning

confidence: 99%

Section: Cbr In Medicinementioning

confidence: 99%

Impact of Genetic Optimization on the Prediction Performance of Case-Based Reasoning Algorithm in Liver Disease

Takkar¹

2017

IJPE

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“…al. [41], a combination of CBR model with clustering is proposed for the better selection of cases in the retrieve step of CBR paradigm. The existing cases of CBR model are clustered into eight groups using K-means clustering.…”

Section: Related Workmentioning

confidence: 99%

A cybernetic systems approach to abnormality detection in retina images using case based reasoning

2020

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In this paper, we present a retina abnormality classification framework for diabetic retinopathy and age related macular degeneration using content based image retrieval. This is performed in two phases, namely, feature extraction and pattern recognition. In the first phase, image pre-processing and Otsu multi-level thresholding is applied to retina fundus images to extract eleven low level spatial and statistical features. The second phase consists of machine learning based classification with these features using four machine learning classifiers, namely, Naive Bayes classifier, support vector machine, K-nearest neighbour classifier and random forest classifier. It is found that random forest classifier outperforms all the other classifiers for the detection of both bright and dark lesion classes and achieves 94.8% and 95.1% accuracy, respectively with ROC area 0.977 and 0.98, respectively.

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“…A CBR system to provide treatment planning in brain cancer radiotherapy was presented by Jagannathan et al (2010). Khussainova et al (2015) designed a CBR application to help medical staff to plan treatment for brain cancer patients using radiotherapy. CBR is also used to segment the images of the kidneys deformed by nephroblastoma (Marieusing et al, 2018).…”

Section: Case-based Reasoning Using Radiographic Imagesmentioning

confidence: 99%

Integrating Radiology into an AI System for Physician Decision Making

Paruchuri¹,

Granville²

2020

CIS

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Over time, physicians gather a vast amount of data such as radiographic images, medical procedures, treatments, and insurance coverage about the patients. A Case-Based Reasoning (CBR) system is developed to organize and retrieve such data to aid physicians in making a diagnosis and formulating a treatment plan for patients with similar traits. This research is an extension of the earlier work presented by the authors. Our earlier research did not consider radiographic images, although radiology plays a vital role in diagnosing and treating patients. Currently, a new algorithm is created to retrieve and organize information from the CBR system to add radiographic images to the patient’s case data. This system is tested and the performance results prove that the acceptance rate achieved by this system is higher than that of the earlier system.

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Retrieval with Clustering in a Case-Based Reasoning System for Radiotherapy Treatment Planning

Cited by 7 publications

References 8 publications

Impact of Genetic Optimization on the Prediction Performance of Case-Based Reasoning Algorithm in Liver Disease

Impact of Genetic Optimization on the Prediction Performance of Case-Based Reasoning Algorithm in Liver Disease

A cybernetic systems approach to abnormality detection in retina images using case based reasoning

Integrating Radiology into an AI System for Physician Decision Making

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