A Deep-Learning Approach for Non-Invasive Temperature Measurements Using 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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Diagnosis of Significant Liver Fibrosis in Patients With Chronic Hepatitis B By Using a Deep Learning-Based Data Integration Network

10.21203/rs.3.rs-1052634/v1 ◽

2021 ◽

Author(s):

Zhong Liu ◽

Huiying Wen ◽

Ziqi Zhu ◽

Qinyuan Li ◽

Li Liu ◽

...

Keyword(s):

Chronic Hepatitis ◽

Deep Learning ◽

Liver Fibrosis ◽

Liver Stiffness ◽

Liver Parenchyma ◽

Ultrasound Images ◽

Clinical Parameters ◽

Non Invasive ◽

Deep Learning Model ◽

Significant Liver Fibrosis

Abstract Background and aims: Chronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥F2) in CHB patients is clinically very important. This study aimed to assess the potential of the joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients’ clinical parameters in a deep learning model to improve the diagnosis of ≥F2 in CHB patients. Methods Of 527 CHB patients who underwent US examination, liver elastography and biopsy, 284 eligible patients were included. We developed a deep learning-based data integration network (DI-Net) to fuse the information of ultrasound images of liver parenchyma, liver stiffness values and patients’ clinical parameters for diagnosing ≥F2 in CHB patients. The performance of DI-Net was cross-validated in a main cohort (n =155) of the included patients and externally validated in an independent cohort (n = 129), with comparisons against single-source data-based models and other non-invasive methods in terms of the area under the receiver-operating-characteristic curve (AUC). Results DI-Net achieved an AUC of 0.943 (95% confidence interval [CI]: 0.893-0.973) in the cross-validation, and an AUC of 0.901 (95% CI: 0.834-0.945) in the external validation, which were significantly greater than those of the comparative methods (AUC ranges: 0.774-0.877 and 0.741-0.848 for cross- and external validations, respectively, Ps < 0.01). Conclusion The joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients’ clinical parameters in a deep learning model could significantly improve the diagnosis of ≥F2 in CHB patients.

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Comparison of Object Detection Approaches Applied to Field Images of Papanicolaou Stained Cytology Slides

10.1101/2021.08.25.21262605 ◽

2021 ◽

Author(s):

André Victória Matias ◽

Allan Cerentini ◽

Luiz Antonio Buschetto Macarini ◽

João Gustavo Atkinson Amorim ◽

Felipe Perozzo Daltoé ◽

...

Keyword(s):

Cervical Cancer ◽

Deep Learning ◽

Oral Cancer ◽

Global Health ◽

Object Detection ◽

Learning Approach ◽

Non Invasive ◽

Global Health Issue ◽

Invasive Method

Papanicolaou is an inexpensive and non-invasive method, generally applied to detect cervical cancer, that can also be useful to detect cancer on oral cavities. Although oral cancer is considered a global health issue with 350.000 people diagnosed over a year it can successfully be treated if diagnosed at early stages. The manual process of analyzing cells to detect abnormalities is time-consuming and subject to variations in perceptions from different professionals. To evaluate a possible solution to the automation of this process, in this paper we employ the object detection deep learning approach in the analysis of this type of image using 3 models: RetinaNet, Faster R-CNN, and Mask R-CNN. We trained and tested the models using images from 6 cytology slides (4 cancer cases and 2 healthy samples) and our results show that Mask R-CNN was the best model for localization and classification of nuclei with an IoU of 0.51 and recall of abnormal nuclei of 0.67.

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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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