مقاله انگلیسی رایگان در مورد تشخیص تومورهای پستان با ماشین بردار پشتیبانی – الزویر ۲۰۲۰

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله تشخیص تومورهای پستان بر اساس استخراج ویژگی لبه با استفاده از ماشین بردار پشتیبانی
عنوان انگلیسی مقاله Breast tumors recognition based on edge feature extraction using support vector machine
انتشار مقاله سال ۲۰۲۰
تعداد صفحات مقاله انگلیسی ۸ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۳٫۸۳۰ در سال ۲۰۱۹
شاخص H_index ۵۱ در سال ۲۰۲۰
شاخص SJR ۰٫۷۱۱ در سال ۲۰۱۹
شناسه ISSN ۱۷۴۶-۸۰۹۴
شاخص Quartile (چارک) Q2 در سال ۲۰۱۹
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط پزشکی، مهندسی پزشکی
گرایش های مرتبط جراحی زنان و زایمان، بیوالکتریک، پردازش تصاویر پزشکی
نوع ارائه مقاله
ژورنال
مجله  پردازش و کنترل سیگنال زیست پزشکی – Biomedical Signal Processing and Control
دانشگاه  College of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, 211167, China
کلمات کلیدی تومور بدخیم، تومور خوش خیم، ویژگی مورفولوژیکی، طبقه بندی تومور
کلمات کلیدی انگلیسی Malignant tumor، Benign tumor، Morphologic feature، Tumor classification
شناسه دیجیتال – doi
https://doi.org/10.1016/j.bspc.2019.101825
کد محصول E14202
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
Graphical abstract
۱٫ Introduction
۲٫ Material and methods
۳٫ Results
۴٫ Discussion
۵٫ Conclusions
Declaration of Competing Interest
CRediT authorship contribution statement
Acknowledgments
References

بخشی از متن مقاله:
Abstract

Nowadays, it is important for the detection of ultrasound images of breast tumors. In this paper, a new ultrasonic image feature extraction algorithm combining edge-based features and morphologic feature information is proposed, which has good effect on benign and malignant identification of breast tumors. This paper mainly studies three features (Sum of maximum curvature, Sum of maximum curvature and peak, Sum of maximum curvature and standard deviation) according to the shape histogram of ultrasound breast tumors from a local perspective. Based on the results of SVM classifier, it was found that the edgebased features have higher classification accuracy. The recognition system would perform better when morphologic features (Roughness, Regularity, Aspect ratio, Ellipticity, Roundness) were incorporated, compared with the control group whose input only with morphologic features. The results show that edge-based features can well describe breast tumors in ultrasound images, and have the potential to be used in breast ultrasound computer-aided design.

Introduction

The statistic report in 2017 shows that the average age of breast cancer patients in China is 48.7 [1]. Breast cancer has become a common disease among women in the current society [2]. Both the breast cancer’s morbidity and mortality are higher than that of other female malignant tumors. Clinical studies have shown that early detection and effective treatments can greatly improve the survival rate of female patients. However, there was no obvious symptoms in the initial when the patient got the cancer, which makes the detection difficult. Therefore, how to discover the lesion area of breast as early as possible so as to improve the cure rate of breast cancer has become a very important topic in the medical field. Ultrasound imaging is a convenient, low-cost, effective, realtime, non-radiation imaging tool, which has been widely used in clinical breast cancer detection [3,4]. In breast tumor diagnosis, the breast ultrasound computer-aided diagnosis (CAD) has been becoming more and more important. It performs better in image preprocessing, segmentation, feature extraction and selection, and tumor classification, including the objective evaluation ∗ Corresponding author. E-mail address: tongying@njit.edu.cn (Y. Tong). results, the classification accuracy, and the diagnostic sensitivity [5,6]. The extraction of different features is crucial in breast ultrasound CAD. Over the past years, research prevailingly concerned the morphologic feature extraction and texture feature extraction [7–۹]. Texture features mainly reflect the surface properties of objects through pixels’ gray distribution and their surrounding spatial neighborhoods’ properties like the clarity, thickness and depth of the image texture.

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