Frontiers in cellular and infection microbiology · 2026
DETECTX: a novel deep learning approach for detecting pneumonia, tuberculosis, and aspergillosis from chest X-rays.
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Wang Xian-Hong, Ahmed Naeem, Saeed Muhammad, Ali Hassan, Chen Guang-Wei, Sun Shi-Chang, Dong Wei-Xia, Teng Tie-Shan, Liao Zhong-Xia, Saddozai Umair Ali Khan
Аннотация
BACKGROUND: Aspergillosis, pneumonia, and tuberculosis are distinct respiratory illnesses, but they share similar symptoms, including cough, fever, and shortness of breath. This overlap can make accurate diagnosis challenging. Delayed or incorrect identification, particularly of aspergillosis, can lead to worsening patient outcomes and significant lung damage.
METHODS: To address this diagnostic challenge, we developed a deep learning based system designed to differentiate between aspergillosis, pneumonia, tuberculosis, and healthy lungs using chest X-ray images. We implemented three models: Convolutional Neural Networks (CNN), ResNet-50, and a novel Siamese Convolutional Neural Network. The models were trained on a dataset of 7,200 images, categorized into four groups: aspergillosis, pneumonia, tuberculosis, and healthy. We utilized various training-testing ratios to optimize performance, which was evaluated using accuracy, precision, recall, F1-score, and confusion matrices.
RESULTS: Among the models tested, the Siamese neural network outperformed the others, achieving an accuracy of 98.72%, surpassing the performance of state-of-the-art models in comparative studies.
CONCLUSION: The novel Siamese CNN model demonstrated excellent accuracy in differentiating respiratory diseases from chest X-ray images, offering a promising tool for more accurate and timely diagnoses. We also developed an intuitive web interface that enables healthcare providers to upload and analyze medical images, facilitating quicker and more reliable diagnoses to support timely interventions.
METHODS: To address this diagnostic challenge, we developed a deep learning based system designed to differentiate between aspergillosis, pneumonia, tuberculosis, and healthy lungs using chest X-ray images. We implemented three models: Convolutional Neural Networks (CNN), ResNet-50, and a novel Siamese Convolutional Neural Network. The models were trained on a dataset of 7,200 images, categorized into four groups: aspergillosis, pneumonia, tuberculosis, and healthy. We utilized various training-testing ratios to optimize performance, which was evaluated using accuracy, precision, recall, F1-score, and confusion matrices.
RESULTS: Among the models tested, the Siamese neural network outperformed the others, achieving an accuracy of 98.72%, surpassing the performance of state-of-the-art models in comparative studies.
CONCLUSION: The novel Siamese CNN model demonstrated excellent accuracy in differentiating respiratory diseases from chest X-ray images, offering a promising tool for more accurate and timely diagnoses. We also developed an intuitive web interface that enables healthcare providers to upload and analyze medical images, facilitating quicker and more reliable diagnoses to support timely interventions.