Screening Thyroid Tumor in Ultrasound Thyroid Images using Hidden Markov Model

Shankarlal, B.; Sathya, P. D.; Sakthivel, V.

doi:10.1109/icicv50876.2021.9388632

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Physicians typically make a preliminary diagnosis of thyroid cancer based on the FNAB of thyroid nodules [11]. Although FNAB is currently considered the gold standard for preoperative diagnosis of malignant tumors, 20-30% of FNABs are reported to be non-diagnostic or indeterminate due to their inherent limitations [12][13]. The process of diagnosing thyroid nodule pathology is complex, meticulous, and expensive.…”

Section: Discussionmentioning

confidence: 99%

Thyroid cancer pathological classification in ultrasound images based on faster R-CNN network

Shiyang,

ZeNan,

Chen

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Objective: This study explored the performance of deep learning Faster R-CNN network for the automatic detection of nodules in thyroid ultrasound images and its efficacy in predicting different pathological classifications of thyroid malignant nodules. Methods:We retrospectively collected 1762 thyroid cancer ultrasound images from 548 patients, of which 80% of the enhanced ultrasound images were used for training and testing, and the remaining 20% were used for validation. The pathological classification of all nodules was confirmed by pathology. We also evaluated the effectiveness of Faster R-CNN models based on different backbone networks in predicting the pathological classification of thyroid cancer. Results:The Faster R-CNN model using ResNet50 as the backbone network in this study had an AUROC of 0.873 and an mAP of 84.04% for pathological classification of malignant thyroid nodules. The accuracy rate for Papillary Thyroid Carcinoma (PTC) was 84.89%, for Medullary Thyroid Carcinoma (MTC) was 89.41%, for Anaplastic Thyroid Carcinoma (ATC) was 82.38%, and for Follicular Thyroid Carcinoma (FTC) was 81.36%. The overall accuracy rate of the final classification was 84.59%. The recall rates of the best model were 87.37%, 85.80%, 83.48%, and 82.30% for PTC, MTC, ATC, and FTC, respectively. Conclusion:This study demonstrates that deep learning Faster R-CNN network can detect thyroid nodules and has good diagnostic efficacy in distinguishing pathological classifications of thyroid malignant nodules. It has great potential for clinical applications.

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Section: Discussionmentioning

confidence: 99%

Thyroid cancer pathological classification in ultrasound images based on faster R-CNN network

Shiyang,

ZeNan,

Chen

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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“…In [17], thyroid papillary cancer identification using modified faster R-CNN is proposed. Recently, authors have [18] applied Hidden Markov Model for detection and segmentation of thyroid region from ultrasound images. In [19], authors used ANN to classify thyroid nodules using Region of Interest (RoI) from blocks for texture calculation and for segmentation they use Active Contour without Edge (ACwE).…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Technique for Detecting Various Thyroid Diseases Using Deep Learning

Prathibha¹,

Dahiya²,

Robin³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Thyroid disease is a medical condition caused due to the excess release of thyroid hormone. It is released by the thyroid gland which is in front of the neck just below the larynx. Medical pictures such as X-rays and CT scans can, however, be used to diagnose it. In this proposed model, Deep Learning technology is used to detect thyroid diseases. A Convolution Neural Network (CNN) based modified ResNet architecture is employed to detect five different types of thyroid diseases namely 1. Hypothyroid 2. Hyperthyroid 3. Thyroid cancer 4. Thyroiditis 5. Thyroid nodules. In the proposed work, the training method is enhanced using dual optimizers for better accuracy and results. Keras, a Python library that is high level runs as the main part of the Tensor Flow framework. It is used in the proposed work to implement deep learning techniques. The comparative analysis of the proposed model and the existing work helps to show that there is a great improvement in the performance metrics in classifying the type of thyroid disease. By applying Adam and SGD (Stochastic Gradient Descent) optimizers in the training phase of the proposed model it was identified that these increase the operational efficiency of the modified ResNet model. After retraining the model with SGD, the modified ResNet provides more accuracy of about 97% whereas the basic ResNet architecture attains 94% accuracy. A web-based framework is also developed which yields the type of thyroid disease as the output for a given input scanned image of the system.

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