Predictive Modeling for Thyroid Disease Diagnosis using Machine Learning

Medical image classification, a critical component in medical diagnostics, has significantly advanced through the integration of machine learning (ML) and deep learning (DL) techniques. This review comprehensively explores the evolution, methodologies, and applications of ML and DL in medical image classification. Traditional ML methods, including support vector machines and decision trees, have provided a foundation for early advancements by utilizing handcrafted features. However, the advent of DL, particularly convolutional neural networks (CNNs), has revolutionized the field by enabling automatic feature extraction and achieving superior performance. This review examines various DL architectures, such as ResNet, VGG, and Inception, highlighting their contributions to tasks like tumor detection, organ segmentation, and disease classification. Furthermore, it addresses challenges like data scarcity, interpretability, and computational demands, discussing potential solutions like data augmentation, transfer learning, and model optimization. The review also considers the ethical implications and the need for robust validation to ensure clinical applicability. Through a comparative analysis of existing studies, this review underscores the transformative impact of ML and DL on medical imaging, emphasizing the continuous need for innovation and interdisciplinary collaboration to enhance diagnostic accuracy and patient outcomes.

show abstract

Survey on Systematic Analysis of Deep Learning Models Compare to Machine Learning

Degadwala,

Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

View full text Add to dashboard Cite

This survey provides a comprehensive analysis of the systematic differences and advancements between deep learning (DL) and traditional machine learning (ML) models. By examining a wide array of research papers, the study highlights the unique strengths and applications of both methodologies. Deep learning, with its multi-layered neural networks, excels in handling large, unstructured datasets, making significant strides in image and speech recognition, natural language processing, and complex pattern recognition tasks. Conversely, traditional machine learning models, which rely on feature extraction and simpler algorithms, remain highly effective in structured data scenarios such as classification, regression, and clustering problems. The survey elucidates the criteria for choosing between DL and ML, focusing on factors like data size, computational resources, and specific application requirements. Furthermore, it discusses the evolving landscape of hybrid models that integrate DL and ML techniques to leverage the strengths of both approaches. This analysis provides valuable insights for researchers and practitioners aiming to deploy the most suitable AI models for their specific needs, emphasizing the importance of contextual understanding in the rapidly advancing field of artificial intelligence.

show abstract

Predictive Modeling for Thyroid Disease Diagnosis using Machine Learning

Cited by 7 publications

References 15 publications

Early Prediction of Thyroid Cancer using Hybrid Combination of Swarm Optimization and Meta Classifier based Machine Learning Algorithm

Early Prediction of Thyroid Cancer using Hybrid Combination of Swarm Optimization and Meta Classifier based Machine Learning Algorithm

A Review on Machine Learning and Deep Learning Methods on Medical Image Classification

Survey on Systematic Analysis of Deep Learning Models Compare to Machine Learning

Contact Info

Product

Resources

About