This paper introduces Neural Network Console, a graphical user interface (GUI)-based integrated development environment for deep learning developers. Neural Network Console helps deep learning beginners learn deep learning technology quickly, while enabling experts to develop deep learning applications efficiently. Neural Network Console can be used for various purposes such as image signal processing, image prediction / complementation, image generation, image abnormality detection, imaging control as well as image recognition.
A cancer treatment plan has been determined based on TNM classification by considering patient's age and medical history. However, the ability to predict recurrence risk would be contributed for the selection of an appropriate treatment and follow-up plan. The purpose of this study is to develop a method for the prediction of recurrence risk of patients with lung cancer by using pattern recognition. The public database NSCLC-Radiogenomics was used in this study. Sixty one patients (24 recurrences and 37 no recurrences) selected from the public database, and their pretreatment CT images were obtained. First, we selected one slice from the largest tumor area and segmented the tumor region manually. We subsequently determined 367 radiomic features. Seven radiomic features were selected by using least absolute shrinkage and selection operator (Lasso). Linear discriminant analysis (LDA) and support vector machine (SVM) with 7 radiomic features were employed for the estimation of recurrence risk. The experimental result showed that the area under the curve (AUC) values were 0.79 with LDA and 0.91 with SVM, respectively. Our scheme can predict the recurrence risk of lung cancers by using non-invasive image examination. However, we found that pattern recognition was not practical for the prediction problems containing censored time.