scholarly journals Comparison of Deep Learning Algorithms for Semantic Segmentation of Ultrasound Thyroid Nodules

2021 ◽  
Vol 7 (2) ◽  
pp. 879-882
Author(s):  
Elmer Jeto Gomes Ataide ◽  
Shubham Agrawal ◽  
Aishwarya Jauhari ◽  
Axel Boese ◽  
Alfredol Illanes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Thyroid Nodules ◽  
Performance Metrics ◽  
Diagnostic System ◽  
Diagnostic Tools ◽  
Observer Variability ◽  
Dice Coefficient ◽  
Computer Aided

Abstract Ultrasound (US) imaging is used as a preliminary diagnostic tool for the detection, risk-stratification and classification of thyroid nodules. In order to perform the risk stratification of nodules in US images physicians first need to effectively detect the nodules. This process is affected due to the presence of inter-observer and intra-observer variability and subjectivity. Computer Aided Diagnostic tools prove to be a step in the right direction towards reducing the issue of subjectivity and observer variability. Several segmentation techniques have been proposed, from these Deep Learning techniques have yielded promising results. This work presents a comparison between four state of the art (SOTA) Deep Learning segmentation algorithms (UNet, SUMNet, ResUNet and Attention UNet). Each network was trained on the same dataset and the results are compared using performance metrics such as accuracy, dice coefficient and Intersection over Union (IoU) to determine the most effective in terms of thyroid nodule segmentation in US images. It was found that ResUNet performed the best with an accuracy, dice coefficient and IoU of 89.2%, 0.857, 0.767. The aim is to use the trained algorithm in the development of a Computer Aided Diagnostic system for the detection, riskstratification and classification of thyroid nodules using US images to reduce subjectivity and observer variability

scholarly journals Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

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.

scholarly journals Benchmarking feature quality assurance strategies for non-targeted metabolomics

2021 ◽  
Author(s):  
Yasin El Abiead ◽  
Maximilian Milford ◽  
Harald Schoeny ◽  
Mate Rusz ◽  
Reza M Salek ◽  
...  
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Liquid Chromatography ◽  
Deep Learning ◽  
Performance Metrics ◽  
Targeted Metabolomics ◽  
Mass Spec ◽  
Chromatographic Peaks ◽  
Resolution Mass

Automated data pre-processing (DPP) forms the basis of any liquid chromatography-high resolution mass spec-trometry-driven non-targeted metabolomics experiment. However, current strategies for quality control of this im-portant step have rarely been investigated or even discussed. We exemplified how reliable benchmark peak lists could be generated for eleven publicly available datasets acquired across different instrumental platforms. Moreover, we demonstrated how these benchmarks can be utilized to derive performance metrics for DPP and tested whether these metrics can be generalized for entire datasets. Relying on this principle, we cross-validated different strategies for quality assurance of DPP, including manual parameter adjustment, variance of replicate injection-based metrics, unsupervised clustering performance, automated parameter optimization, and deep learning-based classification of chromatographic peaks. Overall, we want to highlight the importance of assessing DPP performance on a regular basis.

Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images

2020 ◽  
Vol 47 (9) ◽  
pp. 3952-3960 ◽  
Author(s):  
Chao Sun ◽  
Yukang Zhang ◽  
Qing Chang ◽  
Tianjiao Liu ◽  
Shaohang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4373 ◽  
Author(s):  
Zabit Hameed ◽  
Sofia Zahia ◽  
Begonya Garcia-Zapirain ◽  
José Javier Aguirre ◽  
Ana María Vanegas
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Diagnostic System ◽  
Microscopic Analysis ◽  
Classification Performance ◽  
Histopathological Analysis ◽  
Learning Approach ◽  
Ensemble Strategy ◽  
Histopathology Images

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.

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