Viral Pneumonia Detection from Chest X-rays using GLCM and LBP features DOI

Param Shah,

Deepa Sankar

Published: Dec. 1, 2023

The early contact less detection of viral pneumonia is important as the virus have ability to mutate and adapt frequently resulting in an epidemic situation or potential pandemic a short time. This work unveils technique for identifying from chest X-rays. A combination Gray Level Co-occurrence Matrix (GLCM) Local Binary Pattern (LBP) features with Support Vector Machine (SVM) classifier used detection. effect various classifiers feature combinations on are also assessed. From experimental results, GLCM LBP along SVM gives best result accuracy 90.5% F1 score 0.9073 compared stat-of-the-art.

Language: Английский

User Friendly Web Based Deep Learning System for TB and Pneumonia Diagnosis in Developing Countries DOI

Maymun Elmi Dirie,

Hani Abdi Ali,

Ahmed Abdulkadir Mohamed

et al.

Published: Nov. 1, 2023

Tuberculosis (TB) and pneumonia are two of the leading causes death disability worldwide. Both diseases preventable treatable, but early diagnosis treatment crucial, especially in developing countries. Chest X-ray (CXR) is most widely used imaging modality for diagnosing TB pneumonia, it time-consuming subjective to interpret. Deep learning a subfield within domain machine that uses artificial neural networks as its primary computational framework acquiring knowledge from data. models have demonstrated efficacy range medical applications, encompassing accurate detection tuberculosis pneumonia. This paper proposes deep learning-based system accurately efficiently CXR images using VGG19 architecture. The was trained evaluated on large dataset patients with TB, normal cases, achieving an accuracy 99%. authors also performance eight different algorithms classification abnormal images. algorithm achieved highest (99%), followed by DenseNet121 (98%) Inception V3 (97%). user-friendly accessible through web interface, making healthcare professionals all settings, including suggested method has potential greatly enhance treatment., By automating image analysis process improving accuracy, can help reduce mortality morbidity associated these

Language: Английский

Citations

0
Viral Pneumonia Detection from Chest X-rays using GLCM and LBP features DOI

Param Shah,

Deepa Sankar

Published: Dec. 1, 2023

The early contact less detection of viral pneumonia is important as the virus have ability to mutate and adapt frequently resulting in an epidemic situation or potential pandemic a short time. This work unveils technique for identifying from chest X-rays. A combination Gray Level Co-occurrence Matrix (GLCM) Local Binary Pattern (LBP) features with Support Vector Machine (SVM) classifier used detection. effect various classifiers feature combinations on are also assessed. From experimental results, GLCM LBP along SVM gives best result accuracy 90.5% F1 score 0.9073 compared stat-of-the-art.

Language: Английский

Citations

0