Performance Analysis of Thyroid Tumor Detection and Segmentation Using PCA-Based Random Classification Method

Shankarlal, B.; Sathya, P. D.

doi:10.1007/978-981-15-2256-7_78

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Principal components analysis (PCA): The use of PCA has been highlighted in several studies as a method to reduce the dimensionality of data and decorrelate the attributes of cancer features. For instance, in [69], PCA was applied to the dual-tree complex wavelet (DTCW) transform to select the optimum features of thyroid cancer. In [70], PCA was proposed as a tool for classifying different thyroid cancer subtypes such as papillary, follicular, and undifferentiated.…”

Section: Feature Extraction Methods (Fe)mentioning

confidence: 99%

“…PCA has been widely used in cancer detection and classification of benign and malignant thyroid cells. For example, in [69], PCA was utilized to select the optimal set of wavelet coefficients from the application of the double-tree complex wavelet transform (DTCW) on noisy thyroid images, which were then classified using a random forest (RF). In [70], PCA was applied to data from 399 patients with three types of thyroid carcinoma (papillary, follicular, and undifferentiated) in Morocco, enabling a classification based on factors such as sex, age, type of carcinoma, and region.…”

Section: Preprocessingmentioning

confidence: 99%

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AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

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Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporary frameworks employed in the field, focusing on the objective of AI-driven analysis and dissecting methodologies across supervised, unsupervised, and ensemble learning. Specifically, we delve into techniques such as deep learning, artificial neural networks, traditional classification, and probabilistic models (PMs) under supervised learning. With its prowess in clustering and dimensionality reduction, unsupervised learning (USL) is explored alongside ensemble methods, including bagging and potent boosting algorithms. The thyroid cancer datasets (TCDs) are integral to our discussion, shedding light on vital features and elucidating feature selection and extraction techniques critical for AI-driven diagnostic systems. We lay out the standard assessment criteria across classification, regression, statistical, computer vision, and ranking metrics, punctuating the discourse with a real-world example of thyroid cancer detection using AI. Additionally, this study culminates in a critical analysis, elucidating current limitations and delineating the path forward by highlighting open challenges and prospective research avenues. Through this comprehensive exploration, we aim to offer readers a panoramic view of AI’s transformative role in thyroid cancer diagnosis, underscoring its potential and pointing toward an optimistic future.

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Section: Feature Extraction Methods (Fe)mentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

View full text Add to dashboard Cite

show abstract

“…The top features recommend the model to classify among normal and abnormal data. This helps in reducing the features (Shankarlal & Sathya, 2020). The HFBO algorithm selects the optimal features from the dataset.…”

Section: Feature Selectionmentioning

confidence: 99%

A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine

Sureshkumar¹,

Balasubramaniam²,

Ravi

et al. 2021

Expert Systems

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Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm-based feature selection design for thyroid disease classifier with rough type-2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top-n features. The proposed hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well-known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixedkernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO-RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.

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Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture

Zheng

Qin²,

Cui

et al. 2023

BMC Med Imaging

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Background Identifying thyroid nodules’ boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and glands. Methods The 5822 ultrasound images used in the experiment came from two centers, 4658 images were used as the training dataset, and 1164 images were used as the independent mixed test dataset finally. Based on U-Net, deformable-pyramid split-attention residual U-Net (DSRU-Net) by introducing ResNeSt block, atrous spatial pyramid pooling, and deformable convolution v3 was proposed. This method combined context information and extracts features of interest better, and had advantages in segmenting nodules and glands of different shapes and sizes. Results DSRU-Net obtained 85.8% mean Intersection over Union, 92.5% mean dice coefficient and 94.1% nodule dice coefficient, which were increased by 1.8%, 1.3% and 1.9% compared with U-Net. Conclusions Our method is more capable of identifying and segmenting glands and nodules than the original method, as shown by the results of correlational studies.

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Performance Analysis of Thyroid Tumor Detection and Segmentation Using PCA-Based Random Classification Method

Cited by 3 publications

References 4 publications

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine

Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture

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