Decision Support System for Classification Medullary Thyroid Cancer

Chandio, Jamil Ahmed; Mallah, Ghulam Ali; Shaikh, Noor Ahmed

doi:10.1109/access.2020.3014863

Cited by 11 publications

(6 citation statements)

References 37 publications

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“…Wang et al [24] found 97.34% and 94.42% accuracy in VGG-19 and Inception-ResNet-v2 models, demonstrating the usefulness of sophisticated neural network architectures (see Table III). Chandio et al [39] developed a CNN-based decision support system with 99.00% accuracy for thyroid cancer diagnosis. Hossiny et al [25] achieved 98.74% accuracy using cascaded CNN and split classification.…”

Section: A Comparative Study With Our Proposed Methodsmentioning

confidence: 99%

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Enhancing Thyroid Cancer Diagnostics Through Hybrid Machine Learning and Metabolomics Approaches

Raj

2024

IJACSA

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Thyroid cancer, a prevalent endocrine malignancy, necessitates advanced diagnostic techniques for accurate and early detection. This study introduces an innovative approach that integrates hybrid Machine Learning (ML) algorithms with metabolomics, offering a novel pathway in thyroid cancer diagnostics. Our methodology employs a range of hybrid ML models, combining the strengths of various algorithms to analyze complex metabolomic data effectively. These models include ensemble methods, neural network-based hybrids, and integrations of unsupervised and supervised learning techniques, tailored to decipher the intricate patterns within metabolic profiles associated with thyroid cancer. The study demonstrates how these hybrid ML algorithms can efficiently process and interpret metabolomic data, leading to enhanced diagnostic accuracy. By leveraging the distinct characteristics of each ML model, our approach not only improves the detection of thyroid cancer but also contributes to a deeper understanding of its metabolic underpinnings. The findings of this study pave the way for more personalized and precise medical interventions in thyroid cancer management, showcasing the potential of hybrid ML models in revolutionizing cancer diagnostics. Our system analyzes thyroid cancer metabolomic data using ensemble methods, neural network-based hybrids, and unsupervised and supervised learning integrations. The research shows hybrid ML models may revolutionize cancer diagnoses by improving accuracy. LSTM+CNN, LSTM+GRU, and CNN+GRU have high accuracy rates, helping us comprehend thyroid cancer's biochemical roots. Hybrid ML models enhance thyroid cancer diagnosis and management, enabling more tailored and accurate medical treatments. The hybrid machine learning models like LSTM+CNN, LSTM+GRU, and CNN+GRU beat CNN, VGG-19, Inception-ResNet-v2, decision support, and random forests (99.45%).

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Section: A Comparative Study With Our Proposed Methodsmentioning

confidence: 99%

“…CNNs are powerful deep neural networks for visual analysis. They excel in automatically detecting and learning spatial hierarchies of characteristics from pictures, which is essential for thyroid cancer detection in medical imaging [39]. Convolutional, pooling, and fully linked layers make up a CNN.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Enhancing Thyroid Cancer Diagnostics Through Hybrid Machine Learning and Metabolomics Approaches

Raj

2024

IJACSA

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“…Jamil Ahmed Chandio et al [8]: This paper addresses the classification problem of Medullary Thyroid Cancer and classifies malignant and non-malignant cells and tissues. The proposed methodology of this paper is based upon three layers.…”

Section: Classificationmentioning

confidence: 99%

The Role of Machine Learning in Thyroid Cancer Diagnosis

Kate¹,

Kale²

2023

Advances in Computer Science Research

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Thyroid cancer occurs in the thyroid gland cells. This is butterfly shaped gland which is situated at lower part of neck. Thyroid cancer might not cause any symptoms initially. But as the cancer grows, it causes pain and swelling in neck. To detect and classify abnormalities of the thyroid gland Ultrasound imaging is mostly used. Computer aided diagnosis (CAD) help healthcare sector to increase the diagnosis accuracy and to reduce biopsy ratio. Machine learning techniques play a vital role in diagnosing diseases from the medical database. Various diseases can be predicted early using machine learning techniques. In this study, many previous research works are reviewed which use machine learning techniques to predict thyroid cancer. Machine learning classification techniques like Decision tree Classification, Random Forest Classification, Naïve Bayes, Kernel SVM, K-Nearest Neighbours, Support vector Machines (SVM), Logistic regression etc. are reviewed from the research papers. A short description about machine learning and segmentation is also presented.

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“…Decision Trees are based on the Sum of Product (SOP) representation. Each branch from the tree's core to a leaf node along with the category is an intersection (product) of weights and dissimilar branches terminate in that category form a predicate (sum) (16) . The foremost challenge in executing a decision tree is deciding which features should be recognized for the root node at every level (17) - (18) .…”

Section: 3decision Treementioning

confidence: 99%

A Novel Classification Methodology for Thyroid Cancer using C4.5 with Firefly Optimization Algorithm (CFOA)

Vanitha¹,

Perumal²

2022

IJST

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Objectives:The main objective of this research is to detect thyroid cancer in its early stages and to improve the accuracy using a novel method C4.5 with Firefly Optimization Algorithm (CFOA). This research also focuses on developing an effective machine learning-based accurate prediction model. Methods: The images in this investigation are associated to thyroid disorder with 4672 samples of people including both females and males having hypothyroidism and hyperthyroidism, as well as healthy people without thyroid disorder. The data was gathered for one year with the primary goal of classifying thyroid disease using machine learning (ML) algorithms. These data comprise of Gender, Age, Thyroid hormone (T4), Triiodothyronine (T3), Thyroid stimulating hormone (TSH), etc. The performance of this proposed method was measured using parameters such as accuracy, precision, F1-score and recall. Findings: The performance of this proposed method was assessed with the state-ofthe-art existing methods like Naive Bayes (NB) algorithm, K-Nearest Neighbor (KNN) Algorithm and Adaboost. The proposed algorithm showed maximum precision of 0.9935, recall of 0.9971, F1-score of 0.9951 and accuracy of 0.9981 respectively when compared with the existing algorithms. Novelty: A novel algorithm C4.5 with Firefly Optimization Algorithm was proposed in this paper to speed-up and to increase the effectiveness of the machine learning algorithm. Moreover, this study focuses on developing an accurate prediction model by comparing classification algorithms based on accuracy and confusion matrices and then identifying the most effective classifier based on performance.

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Decision Support System for Classification Medullary Thyroid Cancer

Cited by 11 publications

References 37 publications

Enhancing Thyroid Cancer Diagnostics Through Hybrid Machine Learning and Metabolomics Approaches

Enhancing Thyroid Cancer Diagnostics Through Hybrid Machine Learning and Metabolomics Approaches

The Role of Machine Learning in Thyroid Cancer Diagnosis

A Novel Classification Methodology for Thyroid Cancer using C4.5 with Firefly Optimization Algorithm (CFOA)

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