scholarly journals Breast Tissue Characterization in X-Ray and Ultrasound Images using Fuzzy Local Directional Patterns and Support Vector Machines

Author(s):  
Mohamed Abdel-Nasser ◽  
Domenec Puig ◽  
Antonio Moreno ◽  
Adel Saleh ◽  
Joan Marti ◽  
...  
2004 ◽  
Vol 48 (2) ◽  
pp. 223-236 ◽  
Author(s):  
Daniel J. Strauß ◽  
Gabriele Steidl ◽  
Udo Welzel

2016 ◽  
Vol 32 (3) ◽  
pp. 283-292 ◽  
Author(s):  
Carmina Dessana Lima Nascimento ◽  
Sérgio Deodoro de Souza Silva ◽  
Thales Araújo da Silva ◽  
Wagner Coelho de Albuquerque Pereira ◽  
Marly Guimarães Fernandes Costa ◽  
...  

2021 ◽  
Vol 226 (07) ◽  
pp. 190-197
Author(s):  
Võ Thị Một ◽  
Võ Duy Nguyên ◽  
Nguyễn Tấn Trần Minh Khang

COVID-19 gây ra dịch viêm đường hô hấp cấp, có hơn 90 triệu ca lây nhiễm và hơn 2 triệu người chết trên toàn thế giới. Bệnh lây qua đường hô hấp, mỗi ngày có hơn 300 ngàn ca nhiễm mới. Trong nghiên cứu này, chúng tôi khảo sát các đặc trưng học sâu trên ảnh X-Quang phổi và sử dụng các phương pháp máy học truyền thống bao gồm k-Nearest-Neighbours (k-NN), Support Vector Machines (SVM), Logistic `Regression cho bài toán phân loại ảnh X-Quang vào 3 lớp covid-19, pneumonia, normal. Kết quả đánh giá trên bộ dữ liệu gồm 3423 ảnh X-quang phổi được tổng hợp từ 4 bộ dữ liệu COVID-19 Radiography Database, Covid-19 Image Dataset, COVID-19 PatientsLungs X Ray Images 10000, COVID19 High quality images công bố năm 2020, các kết quả thực nghiệm, phân tích đánh giá được chỉ ra chi tiết là cơ sở cho các nghiên cứu tiếp theo.


2010 ◽  
Vol 22 (02) ◽  
pp. 81-89 ◽  
Author(s):  
Chuan-Yu Chang ◽  
Hsin-Cheng Huang ◽  
Shao-Jer Chen

Heterogeneous thyroid nodules have distinct components and vague boundaries in ultrasound (US) images. It is difficult for radiologists and physicians to manually draw the complete shape of a nodule, or distinguish what kind of components a nodule has. Hence, this article presents an automatic process for nodule segmentation and component classification. A decision-tree algorithm is used to segment the possible nodular area. A refinement process is then applied to recover the nodular shape. Finally, a hierarchical method based on support vector machines (SVMs) is used to identify the components in the nodular lesion. Experimental results of the proposed approach were compared with those of other methods.


2019 ◽  
Vol 5 (1) ◽  
pp. 285-287
Author(s):  
Jannis Hagenah ◽  
Sascha Leymann ◽  
Floris Ernst

AbstractInference from medical image data using machine learning still suffers from the disregard of label uncertainty. Usually, medical images are labeled by multiple experts. However, the uncertainty of this training data, assessible as the unity of opinions of observers, is neglected as training is commonly performed on binary decision labels. In this work, we present a novel method to incorporate this label uncertainty into the learning problem using weighted Support Vector Machines (wSVM). The idea is to assign an uncertainty score to each data point. The score is between 0 and 1 and is calculated based on the unity of opinions of all observers, where u = 1 if all observers have the same opinion and u = 0 if the observers opinions are exactly 50/50, with linear interpolation in between. This score is integrated in the Support Vector Machine (SVM) optimization as a weighting of errors made for the corresponding data point. For evaluation, we asked 15 observers to label 48 2D ultrasound images of aortic roots addressing whether the images show a healthy or a pathologically dilated anatomy, where the ground truth was known. As the observers were not trained experts, a high diversity of opinions was present in the data set. We performed image classification using both approaches, i.e. classical SVM and wSVM with integrated uncertainty weighting, utilizing 10-fold Cross Validation, respectively (linear kernel, C = 7). By incorporating the observer uncertainty, the classification accuracy could be improved by 3.1 percentage points (SVM: 83.5%, wSVM: 86.6%). This indicates that integrating information on the observers’ unity of opinions increases the generalization performance of the classifier and that uncertainty weighted wSVM could present a promising method for machine learning in the medical domain.


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