Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images

Abstract Background: Breast cancer is one of the most serious diseases threatening women’s health. Early screening based on ultrasound can help to detect and treat tumors in early stage. However, due to the lack of radiologists with professional skills, ultrasound based breast cancer screening has not been widely used in rural area. Computer-aided diagnosis (CAD) technology can eﬀectively alleviates this problem. Since Breast Ultrasound (BUS) images have low resolution and speckle noise, lesion segmentation, which is an important step in CAD system, is challenging.Results: Two datasets were used for evaluation. Dataset A comprises 500 BUS images from local hospitals, while dataset B comprises 205 BUS images from open source. The experimental results show that the proposed method outperformed its related classic segmentation methods and the state-of-the-art deep learning model, RDAU–NET. And its’ Accuracy(Acc), Dice eﬃcient(DSC) and Jaccard Index(JI) reached 96.25%, 78.4% and 65.34% on dataset A, and ACC, DC and Sen reached 97.96%, 86.25% and 88.79% on dataset B.Conclusions: We proposed an adaptive morphology snake based on marked watershed(AMSMW) algorithm for BUS images segmentation. It was proven to be robust, eﬃcient and eﬀective. In addition, it was found to be more sensitive to malignant lesions than benign lesions. What’s more, since the Rectangular Region of Interest(RROI) in the proposed method is drawn manually, we will consider adding deep learning network to automatically identify RROI, and completely liberate the hands of radiologists.Methods: The proposed method consists of two main steps. In the ﬁrst step, we used Contrast Limited Adaptive Histogram Equalization(CLAHE) and Side Window Filter(SWF) to preprocess BUS images. Therefore, lesion contours can be eﬀectively highlighted and the inﬂuence of noise can be eliminated to a great extent. In the second step, we proposed adaptative morphology snake(AMS) as an embedded segmentation function of AMSMW. It can adjust working parameters adaptively, according to diﬀerent lesions’ size. By combining segmentation results of AMS with marker region obtained by morphological method, we got the marker region of marked watershed (MW). Finally, we obtained candidate contours by MW. And the best lesion contour was chosen by maximum Average Radial Derivative(ARD).

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Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g5163.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 1211-1215

Keyword(s):

Deep Learning ◽

Thyroid Nodule ◽

Thyroid Nodules ◽

Second Opinion ◽

Learning Approach ◽

Ultrasound Images ◽

Medical Ultrasound ◽

Cad System ◽

Improve Accuracy

Ultrasound scanning is most excellent significant diagnosis techniques utilized for thyroid nodules identification. A thyroid nodule is unnecessary cells that can develop in your base of neck which can be normal or cancerous. Many Computer added diagnosis systems (CAD) have been developed as a second opinion for radiologist. The thyroid nodules classification using machine learning and deep learning approach is latest trend which is using to improve accuracy for differentiation of thyroid nodules from benign and malignant type. In this paper we review the most recent work on CAD system which uses different feature extraction technique and classifier used for thyroid nodules classification with deep learning approach. This paper we illustrate the result obtained by these studies and highlight the limitation of each proposed methods. Moreover we summarize convolution neural network (CNN) architecture for classification of thyroid nodule. This literature review is meant at researcher but it also useful for radiologist who is interesting in CAD tool in ultrasound imaging for second opinion.

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