A Review of Thyroid Disorder Detection Using Medical Images

Thyroid is one of the largest endocrine gland. It is a small butterfly shaped gland which is located in the front portion of the neck.It is located just below the Adams apple. Thyroid gland produces hormones that help the body to control metabolism. There are various thyroid disorders.It includes Hyperthyroidism, Hypothyroidism, goiter and thyroid nodules (benign/malignant). is most commonly. Various modalities that are used to detect and classify abnormalities of the thyroid gland are Ultrasoundimaging,(ComputerTomography(CT), Magnetic Resonance Imaging(MRI). Computer Aided Diagnosis (CAD) help radiologists and doctors to increase the diagnosis accuracy, reduce biopsy ratio and save their time and effort. Thyroid medical images are utilized for the diagnosis process

GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classification

Support Vector Machine (SVM) is one of the most popular methods of classification problems due to its global optima solution. However, the selection of appropriate parameters and kernel values remains an obstacle in the process. The problem can be solved by adding the best value of parameter during optimization process in SVM. Gravitational Search Algorithm (GSA) will be used to optimize parameters of SVM. GSA is an optimization algorithm that is inspired by mass interaction and Newton's law of gravity. This research hybridizes the GSA and SVM  to increase system accuracy. The proposed approach had been implemented to improve the classification performance of Thyroid Nodule. The data used in this research are ultrasonography image of Thyroid Nodule obtained from RSUP Dr. Sardjito, Yogyakarta. This research had been evaluated by comparing the default SVM parameters with the proposed method in term of accuracy. The experiment results showed that the use of GSA on SVM is capable to increase system accuracy. In the polynomial kernel the accuracy rose up from 58.5366 % to 89.4309 %, and 41.4634 % to 98.374 % in Polynomial kerne

Classification of histological images of thyroid nodules based on a combination of Deep Features and Machine Learning

Background: Thyroid nodules are a prevalent worldwide disease with complex pathological types. They can be classified as either benign or malignant. This paper presents a tool for automatically classifying histological images of thyroid nodules, with a focus on papillary carcinoma and follicular adenoma. Methods: In this work, two pre-trained Convolutional Neural Network (CNN) architectures, VGG16 and VGG19, are used to extract deep features. Then, a principal component analysis was used to reduce the dimensionality of the vectors. Then, three machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, and Random Forest) were used for classification. These investigations were applied to our database collection, Results: The proposed investigations have been applied to our private database collection with a total of 112 histological images. The highest results were obtained by the VGG16 transfer deep feature and the SVM classifier with an accuracy rate equal to 100%

OPTYMALIZACJA KLASYFIKACJI OBRAZÓW ULTRASONOGRAFICZNYCH TECHNIKĄ TRANSFER LEARNING: STRATEGIE DOSTRAJANIA I WPŁYW KLASYFIKATORA NA WSTĘPNIE WYTRENOWANE WARSTWY WEWNĘTRZNE

Transfer Learning (TL) is a popular deep learning technique used in medical image analysis, especially when data is limited. It leverages pre-trained knowledge from State-Of-The-Art (SOTA) models and applies it to specific applications through Fine-Tuning (FT). However, fine-tuning large models can be time-consuming, and determining which layers to use can be challenging. This study explores different fine-tuning strategies for five SOTA models (VGG16, VGG19, ResNet50, ResNet101, and InceptionV3) pre-trained on ImageNet. It also investigates the impact of the classifier by using a linear SVM for classification. The experiments are performed on four open-access ultrasound datasets related to breast cancer, thyroid nodules cancer, and salivary glands cancer. Results are evaluated using a five-fold stratified cross-validation technique, and metrics like accuracy, precision, and recall are computed. The findings show that fine-tuning 15% of the last layers in ResNet50 and InceptionV3 achieves good results. Using SVM for classification further improves overall performance by 6% for the two best-performing models. This research provides insights into fine-tuning strategies and the importance of the classifier in transfer learning for ultrasound image classification.Transfer Learning (TL) to popularna technika głębokiego uczenia stosowana w analizie obrazów medycznych, zwłaszcza gdy ilość danych jest ograniczona. Wykorzystuje ona wstępnie wyszkoloną wiedzę z modeli State-Of-The-Art (SOTA) i zastosowanie ich do konkretnych aplikacji poprzez dostrajanie (Fine-Tuning – FT). Jednak dostrajanie dużych modeli może być czasochłonne, a określenie, których warstw użyć, może stanowić wyzwanie. W niniejszym badaniu przeanalizowano różne strategie dostrajania dla pięciu modeli SOTA (VGG16, VGG19, ResNet50, ResNet101 i InceptionV3) wstępnie wytrenowanych na ImageNet. Zbadano również wpływ klasyfikatora przy użyciu liniowej SVM do klasyfikacji. Eksperymenty przeprowadzono na czterech ogólnodostępnych zbiorach danych ultrasonograficznych związanych z rakiem piersi, rakiem guzków tarczycy i rakiem gruczołów ślinowych. Wyniki są oceniane przy użyciu techniki pięciowarstwowej walidacji krzyżowej, a wskaźniki takie jak dokładność, precyzja i odzyskiwanie są obliczane. Wyniki pokazują, że dostrojenie 15% ostatnich warstw w ResNet50 i InceptionV3 osiąga dobre wyniki. Użycie SVM do klasyfikacji dodatkowo poprawia ogólną wydajność o 6% dla dwóch najlepszych modeli. Badania te zapewniają informacje na temat strategii dostrajania i znaczenia klasyfikatora w uczeniu transferowym dla klasyfikacji obrazów ultrasonograficznych

Identification of SERPINA1 as single marker for papillary thyroid carcinoma through microarray meta analysis and quantification of its discriminatory power in independent validation

<p>Abstract</p> <p>Background</p> <p>Several DNA microarray based expression signatures for the different clinically relevant thyroid tumor entities have been described over the past few years. However, reproducibility of these signatures is generally low, mainly due to study biases, small sample sizes and the highly multivariate nature of microarrays. While there are new technologies available for a more accurate high throughput expression analysis, we show that there is still a lot of information to be gained from data deposited in public microarray databases. In this study we were aiming (1) to identify potential markers for papillary thyroid carcinomas through meta analysis of public microarray data and (2) to confirm these markers in an independent dataset using an independent technology.</p> <p>Methods</p> <p>We adopted a meta analysis approach for four publicly available microarray datasets on papillary thyroid carcinoma (PTC) nodules versus nodular goitre (NG) from N2-frozen tissue. The methodology included merging of datasets, bias removal using distance weighted discrimination (DWD), feature selection/inference statistics, classification/crossvalidation and gene set enrichment analysis (GSEA). External Validation was performed on an independent dataset using an independent technology, quantitative RT-PCR (RT-qPCR) in our laboratory.</p> <p>Results</p> <p>From meta analysis we identified one gene (SERPINA1) which identifies papillary thyroid carcinoma against benign nodules with 99% accuracy (n = 99, sensitivity = 0.98, specificity = 1, PPV = 1, NPV = 0.98). In the independent validation data, which included not only PTC and NG, but all major histological thyroid entities plus a few variants, SERPINA1 was again markedly up regulated (36-fold, p = 1:3*10^-10) in PTC and identification of papillary carcinoma was possible with 93% accuracy (n = 82, sensitivity = 1, specificity = 0.90, PPV = 0.76, NPV = 1). We also show that the extracellular matrix pathway is strongly activated in the meta analysis data, suggesting an important role of tumor-stroma interaction in the carcinogenesis of papillary thyroid carcinoma.</p> <p>Conclusions</p> <p>We show that valuable new information can be gained from meta analysis of existing microarray data deposited in public repositories. While single microarray studies rarely exhibit a sample number which allows robust feature selection, this can be achieved by combining published data using DWD. This approach is not only efficient, but also very cost-effective. Independent validation shows the validity of the results from this meta analysis and confirms SERPINA1 as a potent mRNA marker for PTC in a total (meta analysis plus validation) of 181 samples.</p

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer-Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a competitive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which de- liver real benefits to patients by improving the diagnosis accuracy while reducing health care cost