A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by Ultrasonography

Abstract

For decades, ultrasound images have been widely used in the detection of various diseases due to their high security and efficiency. However, reading ultrasound images requires years of experience and training. In order to support the diagnosis of clinicians and reduce the workload of doctors, many ultrasonic computer aided diagnostic systems have been proposed. In recent years, the success of deep learning in image classification and segmentation has made more and more scholars realize the potential performance improvement brought by the application of deep learning in ultrasonic computer-aided diagnosis systems. This study is aimed at applying several machine learning algorithms and develop a machine learning method to diagnose subcutaneous cyst. Clinical features are extracted from datasets and images of ultrasonography of 132 patients from Hunan Provincial People’s Hospital in China. All datasets are separated into 70% training and 30% testing. Four kinds of machine learning algorithms including decision tree (DT), support vector machine (SVM), K -nearest neighbors (KNN), and neural networks (NN) had been approached to determine the best performance. Compared with all the results from each feature, SVM achieved the best performance from 91.7% to 100%. Results show that SVM performed the highest accuracy in the diagnosis of subcutaneous cyst by ultrasonography, which provide a good reference in further application to clinical practice of ultrasonography of subcutaneous cyst.

Non-destructive classification of unlabeled cells: Combining an automated benchtop magnetic resonance scanner and artificial intelligence

“…The optimization of the augmentation factor (Fig 7A ) showed a similar behavior as before for MSC augmentation. Each augmentation value tested (5,10,20,40) showed significantly better performance compared to no augmentation. There were no detectable significant differences between the tested augmentation values.…”

“…Histological assessment usually relies on trained personnel performing the sample assessment. As this gives rise to possible human errors, multiple other studies have also introduced unbiased, computer-led methods to support the fast and unbiased diagnosis process in a variety of clinical applications [4][5][6][7]. Although there are other established methods for the identification and characterization of cells and tissues-such as Raman spectroscopy [8][9][10][11][12] or single cell RNA sequencing [13,14]-besides classical histological and immunofluorescent staining, they cannot be performed under sterile conditions or lead to sample destruction.…”

“…Different augmentation parameters were tested to evaluate their individual performance (a). As already described for the MSC classification (Fig 6), nonaugmented data (augmentation factor = 0) provided significantly lower results compared to all augmentation values.There were no significant differences between the compared augmentation factors(5,10,20,40). As the amount of data directly correlated with the need time for training the ANN, it was decided to use an augmentation factor of 5 for subsequent ANN trainings.…”