Deep learning approach for cerebellum localization in prenatal ultrasound images

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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Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

10.54216/fpa.030102 ◽

2021 ◽

pp. 29-42

Author(s):

admin admin ◽

◽

Adnan Mohsin Abdulazeez

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Ultrasound Image ◽

Smart Devices ◽

Learning Approach ◽

Ultrasound Images ◽

Medical Field ◽

Modern Methods

With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.

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Automatic segmentation of the optic nerve in transorbital ultrasound images using a deep learning approach

10.1109/ius52206.2021.9593827 ◽

2021 ◽

Author(s):

Kristen M. Meiburger ◽

Andrea Naldi ◽

Piergiorgio Lochner ◽

Francesco Marzola

Keyword(s):

Deep Learning ◽

Optic Nerve ◽

Automatic Segmentation ◽

Learning Approach ◽

Ultrasound Images

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Deep Learning Based Fast Screening Approach on Ultrasound Images for Thyroid Nodules Diagnosis

Diagnostics ◽

10.3390/diagnostics11122209 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2209

Author(s):

Hafiz Abbad Ur Rehman ◽

Chyi-Yeu Lin ◽

Shun-Feng Su

Keyword(s):

Deep Learning ◽

Thyroid Nodules ◽

Image Database ◽

The United States ◽

Learning Approach ◽

Ultrasound Images ◽

Fast Screening ◽

The World ◽

Competitive Model ◽

Reliable Methods

Thyroid nodules are widespread in the United States and the rest of the world, with a prevalence ranging from 19 to 68%. The problem with nodules is whether they are malignant or benign. Ultrasonography is currently recommended as the initial modality for evaluating thyroid nodules. However, obtaining a good diagnosis from ultrasound imaging depends entirely on the radiologists levels of experience and other circumstances. There is a tremendous demand for automated and more reliable methods to screen ultrasound images more efficiently. This research proposes an efficient and quick detection deep learning approach for thyroid nodules. An open and publicly available dataset, Thyroid Digital Image Database (TDID), is used to determine the robustness of the suggested method. Each image is formatted into a pyramid tile-based data structure, which the proposed VGG-16 model evaluates to provide segmentation results for nodular detection. The proposed method adopts a top-down approach to hierarchically integrate high- and low-level features to distinguish nodules of varied sizes by employing fuse features effectively. The results demonstrated that the proposed method outperformed the U-Net model, achieving an accuracy of 99%, and was two times faster than the competitive model.

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