Development of classification model for thoracic diseases with chest X-ray images using deep convolutional neural network - Okokpujie, Kennedy; Anointing, Tamunowunari-Tasker; Ijeh, Adaora Princess; Okokpujie, Imhade Princess; Ogundele, Mary Oluwafeyisayo; Oguntuyo, Oluwadamilola (Bulletin of Electrical Engineering and Informatics, 2025)
Thoracic disease is a medical condition in the chest wall region. Accurate thoracic disease diagnosis in patients is critical for effective treatment. Atelectasis, mass, pneumonia, and pneumothorax are thoracic diseases that can lead to life-threatening conditions if not detected and treated early enough. When diagnosing these diseases, human expertise can also be susceptible to errors due to fatigue or emotional factors. This research proposes developing a real-time deep learning-based classification model for thoracic diseases. Three deep convolutional neural network (CNN) models - MobileNetV3Large, ResNet-50, and EfficientNetB7 - were evaluated for classifying thoracic diseases from chest X-ray images. The models were tested in 5-class (atelectasis, mass, pneumothorax, pneumonia, and normal), 4-class (atelectasis, pneumothorax, pneumonia, and normal), and 3-class (atelectasis, pneumonia, and normal) modes to assess the impact of high interclass similarity. Retrained MobileNetV3Large achieved the highest classification accuracy: 75.72% next to ResNet-50 (75.2%) and last EfficientNetB7 (73.03%). For the 4-class, EfficientNetB7 (88.08%) led with MobileNetV3Large in the last (87.08%), but MobileNetV3Large led the 3-way with 97.88% with EfficientNetB7 again in the last (96.55%). These results indicate that MobileNetV3 can effectively distinguish and diagnose thoracic diseases from chest X-rays, even with interclass similarity and supports the use of computer-aided detection systems in thoracic disease classification.