Comparative analysis of CNN models in the detection of tuberculosis using chest X-rays
DOI:
https://doi.org/10.35381/i.p.v8i14.4904Keywords:
CNN, diagnosis, tuberculosis, X-ray., (UNESCO Thesaurus).Abstract
Tuberculosis is a critical health problem that affects the lungs. Despite technological advances, its early diagnosis continues to depend on traditional methods with a high degree of subjectivity. This study focuses on comparing different CNN models that support the diagnosis of tuberculosis more accurately and objectively, as a complementary diagnostic method for this disease. The CRISP-DM methodology was used for the training process of the following models: TBNet, DenseNet121, ResNet50, and MobileNetV2. These four convolutional neural network architectures were evaluated taking into account their metrics: accuracy, loss, recall, F1-Score, sensitivity, specificity, and AUC-ROC. The study provides a graphical demonstration of the results of each model, with DenseNet121 obtaining the most stable results among its different metrics, ensuring a favourable contrast between the results classified as false positives and false negatives.
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