Proceedings ArticleDOI
Identification of ovarian mass through ultrasound images using machine learning techniques
Hemita Pathak,Vrushali Kulkarni +1 more
- pp 137-140
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TLDR
Preliminary results depict that the proposed technique can be reliable for ovarian tumor classification as this system is fully automated, advantageous and cost-effective too.Abstract:
Today ovarian cancer is second most perilous cause of cancer deaths in women after breast cancer. In this work, we have developed system which acquires ultrasound images and using image processing and machine learning algorithms accurately classify benign and malignant tumors in ovarian cancer. This technique denoise image using wavelet transform, grey level texture features extracted using GLCM(grey level co-occurrence algorithm), extracted features will be trained through SVM(Support vector machine) and selected non-redundant features selected through Relief-F will be further train and test through SVM for output. Proposed technique was validated by 60 malignant and 60 benign images of patients. On evaluating classifier for 14-texture descriptors give 74% and relief-F gives 82% accuracy. After selecting 6 features from 14 features it will give accuracy 86% and relief-F gives 92% accuracy. Thus, the features are significant for result and preliminary results depict that the proposed technique can be reliable for ovarian tumor classification as this system is fully automated, advantageous and cost-effective too.read more
Citations
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Journal ArticleDOI
Machine learning for medical ultrasound: status, methods, and future opportunities.
Laura J. Brattain,Brian A. Telfer,Manish Dhyani,Manish Dhyani,Joseph R. Grajo,Anthony E. Samir +5 more
TL;DR: Leading machine learning approaches and research directions in US are reviewed, with an emphasis on recent ML advances, and an outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization is presented.
Journal ArticleDOI
Medical Image Segmentation Algorithm Based on Optimized Convolutional Neural Network-Adaptive Dropout Depth Calculation
Feng-Ping An,Jun-e Liu +1 more
TL;DR: The experimental results show that not only are the segmentation effects of the proposed method improved compared with those of the traditional machine learning and other deep learning methods but also the method has a high adaptive segmentation ability for various medical images.
Journal ArticleDOI
Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis
He-Li Xu,Fang-Hua Liu,Hong-Yu Chen,Qian Xiao,Hongjuan Sun,Yu Shi,Song Gao,Yuhong Zhao,Ting-Ting Gong,Qijun Wu +9 more
TL;DR: Wang et al. as mentioned in this paper systematically reviewed articles on the diagnostic performance of AI in ovarian cancer from medical imaging for the first time, and found a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95).
Journal ArticleDOI
Application of entropies for automated diagnosis of abnormalities in ultrasound images: a review
Yuki Hagiwara,Vidya K. Sudarshan,Vidya K. Sudarshan,Sook Sam Leong,Anushya Vijaynanthan,Kwan Hoong Ng +5 more
TL;DR: Diverse types of entropies employed in the development of CAD systems for the diagnosis of abnormalities in the medical images can be used to differentiate the images based on the severity of the abnormalities and for other biomedical applications.
Journal ArticleDOI
Data Engineering for Machine Learning in Women's Imaging and Beyond.
Chen Cui,Shinn-Huey S. Chou,Laura J. Brattain,Laura J. Brattain,Constance D. Lehman,Anthony E. Samir +5 more
TL;DR: The focus of this article is women's imaging; nonetheless, the principles described apply to all domains of medical imaging, including databases, data integrity, and characteristics of data suitable for machine learning projects.
References
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Journal ArticleDOI
Wavelet Based Image Denoising Technique
Sachin D. Ruikar,Dharmpal D Doye +1 more
TL;DR: The main aim is to modify the wavelet coefficients in the new basis, the noise can be removed from the data and a signal to noise ratio as a measure of the quality of denoising was preferred.
Journal ArticleDOI
Despeckling of Ultrasound Medical Images: A Survey
T. Joel,R. Sivakumar +1 more
TL;DR: The main purpose for speckle reduction is to improve the visualization of the image and it is the preprocessing step for segmentation, feature extraction and registration in ultrasound medical imaging.
Proceedings ArticleDOI
Local relative GLRLM-based texture feature extraction for classifying ultrasound medical images
TL;DR: A new approach of extracting local relative texture feature from ultrasound medical images using the Gray Level Run Length Matrix (GLRLM) based global feature using a three level partitioning of images to adapt the traditional global approach of GLfiLM-based feature extraction method.
Proceedings ArticleDOI
Ovarian tumor characterization and classification: A class of GyneScan™ systems
U. Rajendra Acharya,Vinitha Sree S,Luca Saba,Filippo Molinari,Stefano Guerriero,Jasjit S. Suri +5 more
TL;DR: The preliminary results presented in this paper indicate that the proposed technique can be reliably used as an adjunct tool for ovarian tumor classification since the system is accurate, completely automated, cost-effective, and can be easily written as a software application for use in any computer.
Proceedings ArticleDOI
Retrieval and classification of ultrasound images of ovarian cysts combining texture features and histogram moments
Abu Sayeed Md. Sohail,Md. Mahmudur Rahman,Prabir Bhattacharya,Srinivasan Krishnamurthy,Sudhir P. Mudur +4 more
TL;DR: The proposed solution comprises extraction of low level ultrasound image features combining histogram moments with Gray Level Co-Occurrence Matrix (GLCM) based statistical texture descriptors, image retrieval using a similarity model based on Gower's similarity coefficient which measures the relevance between the query image and the target images, and use of multiclass Support Vector Machine (SVM).
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