2022年6月15日 · In this paper, an MBF-CDNN based classification algorithm is proposed for accurate detection of uterine fibroid by means of noise removal. The proposed methodology comprises of input data collection, preprocessing and classification. The preprocessed and contrast-enhanced image is given as the input to the MBF-CDNN classifier.

2022年11月29日 · Dilna et al. [2] used the MBF-CDNN network, which relies on the deep convolutional neural network. Huang et al. [23] used a deep learning −based object detection method to recognize vertebral landmarks. How-ever, there are few studies on uterine fibroid detection and segmentation using deep learning models.

2024年6月1日 · The preprocessed picture was divided into two categories of data: fibroid and non-fibroid, using the MBF-CDNN approach. The proposed model showed an accuracy of 94.73 % for fibroid detection. The aim of this research paper is to design a machine-learning model to denoise the image using filters to remove unnecessary interference in the image ...

2022年1月31日 · Dilna et al. used the MBF-CDNN method to detect uterine fibroids in ultrasound images [27]. Several studies have investigated MRIs of endometrial fibroids using deep-learning-based methods [28 ...

2023年7月1日 · Our research provides an improved YOLOv3 that combines the properties of EfficientNet and YOLOv3, which use a convolutional neural network to extract features, to detect uterine fibroid ultrasound images. Our approach attained an F1 score of 95% and an average precision of 98.38% and reached a detection speed of 0.28 s per image.

Our research provides an improved YOLOv3 that combines the properties of EfficientNet and YOLOv3, which use a convolutional neural network to extract features, to detect uterine fibroid ultrasound images. Our approach attained an F1 score of 95% and an average precision of 98.38% and reached a detection speed of 0.28 s per image.

In this paper, an MBF-CDNN based classifi-cation algorithm is proposed for accurate detection of uterine fibroid by means of noise removal. The proposed methodology comprises of input data collection, pre-processing and classification. The preprocessed and contrast-enhanced image is given as the input to the MBF-CDNN classifier.

approach based convention · Deep Neural Network (MBF-CDNN) 1 Introduction Uterus- the female reproductive system has hollow inside with thick muscular walls. Uterine fibroids (UF) are smooth muscle tumors that develop from the myometrium. Theultrasound(US)imagingtechniqueisused,togetherwithotherimagingtechniques,

Dilna et al. used the MBF-CDNN method to detect uterine fibroids in ultrasound images [27]. ... Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning Article

2022年6月21日 · The preprocessed image is classified into two classes of data: fibroid and non-fibroid, which is done using the MBF-CDNN method. The method is validated using the parameters Sensitivity, specificity, accuracy, precision, F-measure. It is found that the sensitivity is 94.44%, specificity 95% and accuracy 94.736%.