Academic Journal

ResNet18 Supported Inspection of Tuberculosis in Chest Radiographs With Integrated Deep, LBP, and DWT Features

التفاصيل البيبلوغرافية
العنوان: ResNet18 Supported Inspection of Tuberculosis in Chest Radiographs With Integrated Deep, LBP, and DWT Features
المؤلفون: Rajinikanth, Venkatesan, Kadry, Seifedine, Moreno-Ger, Pablo
بيانات النشر: International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
سنة النشر: 2023
المجموعة: Universidad Internacional de La Rioja (UNIR): Re-Unir
مصطلحات موضوعية: algorithms, classification, deep learning, health, IJIMAI, Scopus, JCR
الوصف: The lung is a vital organ in human physiology and disease in lung causes various health issues. The acute disease in lung is a medical emergency and hence several methods are developed and implemented to detect the lung abnormality. Tuberculosis (TB) is one of the common lung disease and premature diagnosis and treatment is necessary to cure the disease with appropriate medication. Clinical level assessment of TB is commonly performed with chest radiographs (X-ray) and the recorded images are then examined to identify TB and its harshness. This research proposes a TB detection framework using integrated optimal deep and handcrafted features. The different stages of this work include (i) X-ray collection and processing, (ii) Pretrained Deep-Learning (PDL) scheme-based feature mining, (iii) Feature extraction with Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT), (iv) Feature optimization with Firefly-Algorithm, (v) Feature ranking and serial concatenation, and (vi) Classification by means of a 5-fold cross confirmation. The result of this study validates that, the ResNet18 scheme helps to achieve a better accuracy with SoftMax (95.2%) classifier and Decision Tree Classifier (99%) with deep and concatenated features, respectively. Further, overall performance of Decision Tree is better compared to other classifiers.
نوع الوثيقة: article in journal/newspaper
اللغة: English
تدمد: 1989-1660
Relation: vol. 8, nº 2; https://www.ijimai.org/journal/bibcite/reference/3318; https://reunir.unir.net/handle/123456789/14831; https://doi.org/10.9781/ijimai.2023.05.004
DOI: 10.9781/ijimai.2023.05.004
الاتاحة: https://reunir.unir.net/handle/123456789/14831
https://doi.org/10.9781/ijimai.2023.05.004
Rights: openAccess
رقم الانضمام: edsbas.E945353C
قاعدة البيانات: BASE
الوصف
تدمد:19891660
DOI:10.9781/ijimai.2023.05.004