Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning

Abstract

Tumor movements should be accurately predicted to improve delivery accuracy and reduce unnecessary radiation exposure to healthy tissue during radiotherapy. The tumor movements pertaining to respiration are divided into intra-fractional variation occurring in a single treatment session and inter-fractional variation arising between different sessions. Most studies of patients’ respiration movements deal with intra-fractional variation. Previous studies on inter-fractional variation are hardly mathematized and cannot predict movements well due to inconstant variation. Moreover, the computation time of the prediction should be reduced. To overcome these limitations, we propose a new predictor for intra- and inter-fractional data variation, called intra- and inter-fraction fuzzy deep learning (IIFDL), where FDL, equipped with breathing clustering, predicts the movement accurately and decreases the computation time. Through the experimental results, we validated that the IIFDL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods. The results also showed that the IIFDL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively. In addition, the average computation time of IIFDL was 1.54 ms for both intra- and inter-fractional variation, which was much smaller than the existing methods. Therefore, the proposed IIFDL might achieve real-time estimation as well as better tracking techniques in radiotherapy.

Deep Learning: A Review for the Radiation Oncologist

“…The proposed model improved root-mean-square-error by 29.98% and prediction overshoot by 70.93% compared with the other existing methods. The average computing time for intra- and inter-fraction fuzzy deep learning (IIFDL) was 1.54 ms for both intra- and inter-fractional variations, which is smaller than existing methods, confirming the advantages in the use of this technique (28).…”

Respiratory motion prediction using deep convolutional long short-term memory network

“…In spite of the fact that several studies have been presented in recent years about the introduction of respiratory prediction methods based on the use of deep artificial neural networks,[ 20 21 22 23 ] and in particular, the use of deep recurrent networks,[ 20 21 23 ] the current study with the generative nature has the advantage of being able to detect tumor motions in all directions. The error of proposed method and the similarity of the reference and generated images significantly reflect the ability of this kind of network to predict pulmonary movements in the lack of proper preliminary data.…”

“…[ 19 ] Recently, a range of studies has used deep learning to predict lung movements. [ 20 21 22 23 ] Among these, some studies have used the concept of recurrent neural networks (RNN) to develop a model for predicting pulmonary movements. [ 20 21 23 ] Methods proposed have tried predicting the next position of the tumor based on its current position by considering a portion of the data as a training set and the remaining part as a test set.…”

“…The results of the proposed method based on deep LSTM show the superiority of this method over conventional neural networks. In the study of Park et al ,[ 22 ] intra- and inter-fraction fuzzy deep learning method has been used to predict tumor motions due to respiration and also decrease computational time. Using this method improved the RMSE by 29.98%.…”

Enhancing the Precision of Missing Data Imputation in Unperceived Data Set Using Chernaive Classifier

“…[15] can be referred as the statistic classifier algorithm uses gain radio for feature selection and to construct the decision tree. Id3 algorithm [13] overcomes multi-value bias problem when selecting test/split attributes, solves the issue of numeric attribute discretization and stores the classifier model in the form of rules by using a heuristic strategy for easy understanding and memory savings. Experiment results show that the improved Id3 algorithm is superior to the current four classification algorithms (J48, Decision Stump, Random Tree and classical Id3) in terms of accuracy, stability and minor error rate.…”