Scholarlink

<p>Tuberculosis remains a major global health concern. Early detection is essential to improving treatment outcomes for tuberculosis. This project explores the classification of tuberculosis from medical images using machine learning models, including Random Forest, Support Vector Machine (SVM), and Logistic Regression (LR). These models were trained using extracted features, and the accuracy, precision, recall, and AUC-ROC metrics were used to assess the models. The receiver operating characteristic curve’s area (AUC) and characteristics were used to evaluate model performance. The results revealed that the Random Forest model excelled, achieving an AUC-ROC score of 99%, accuracy of 96%, specificity of 97%, precision of 97%, sensitivity (recall) of 94%, and an F1 score of 95%. In contrast, the Support Vector Machine attained an AUC-ROC of 100%, with an accuracy of 97%, specificity of 98%, precision of 98%, sensitivity of 96%, and an F1 score of 97%. The Logistic Regression (LR) model achieved an AUC-ROC of 100%, an accuracy of 99%, a specificity of 100%, a precision of 100%, a sensitivity of 98%, and an F1 score of 99%. The evaluation suggests that the Logistic Regression model displays superior predictive abilities and overall performance, showcasing its efficacy in learning from the features present in tuberculosis susceptibility in this project data. The performance of these models has showcased how effective they are in classifying tuberculosis from Chest-Xray which will help in the early detection and diagnosis of tuberculosis diseases for radiologists and the medical field at large.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Tuberculosis Disease Classification Using Machine Learning-Based Techniques

  • 期刊论文

  • 发表日期：

    2026年5月12日

查看全文
  • Racheal Shade Akinbo,
  • Gladstone Eli Atrakpo,
  • Oladunni Abosede Daramola

摘要

Tuberculosis remains a major global health concern. Early detection is essential to improving treatment outcomes for tuberculosis. This project explores the classification of tuberculosis from medical images using machine learning models, including Random Forest, Support Vector Machine (SVM), and Logistic Regression (LR). These models were trained using extracted features, and the accuracy, precision, recall, and AUC-ROC metrics were used to assess the models. The receiver operating characteristic curve’s area (AUC) and characteristics were used to evaluate model performance. The results revealed that the Random Forest model excelled, achieving an AUC-ROC score of 99%, accuracy of 96%, specificity of 97%, precision of 97%, sensitivity (recall) of 94%, and an F1 score of 95%. In contrast, the Support Vector Machine attained an AUC-ROC of 100%, with an accuracy of 97%, specificity of 98%, precision of 98%, sensitivity of 96%, and an F1 score of 97%. The Logistic Regression (LR) model achieved an AUC-ROC of 100%, an accuracy of 99%, a specificity of 100%, a precision of 100%, a sensitivity of 98%, and an F1 score of 99%. The evaluation suggests that the Logistic Regression model displays superior predictive abilities and overall performance, showcasing its efficacy in learning from the features present in tuberculosis susceptibility in this project data. The performance of these models has showcased how effective they are in classifying tuberculosis from Chest-Xray which will help in the early detection and diagnosis of tuberculosis diseases for radiologists and the medical field at large.