In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.

A-Ram Kim, Sang-Yong Rhee, and Hyeon-Woong Jang. IJFIS 2016;16:13-20. http://dx.doi.org/10.5391/IJFIS.2016.16.1.13

Early detection of tuberculosis plays a critical role in effective treatment management. Like active tuberculosis, early identification of inactive forms such as latent or healed tuberculosis is essential to prevent future reactivation. In this study, we developed a deep-learning-based binary classification model to distinguish between active and inactive tuberculosis cases. Our model architecture incorporated an EfficientNet backbone with an MLP-Mixer classification head and was fine-tuned on a dataset annotated by Cheonan Soonchunhyang Hospital. To enhance predictive performance, we applied transfer learning using weights pre-trained on the JFT-300M dataset via the Noisy Student training method. Unlike conventional models, our approach achieved competitive results, with an accuracy of 96.3%, a sensitivity of 95.9%, and a specificity of 96.6% on the test set. These promising outcomes suggest that our model could serve as a valuable asset to support clinical decision-making and streamline early screening workflows for latent tuberculosis.

This study proposes a unified deep-learning framework for the concurrent classification of Chronic Obstructive Pulmonary Disease (COPD) severity and regression of the FEV1/FVC ratio from chest X-ray (CXR) images. We integrated a ConvNeXt-Large backbone with a Slot Attention mechanism to effectively disentangle and refine disease-relevant features for multi-task learning. Evaluation on a clinical dataset demonstrated that the proposed model with a 5-slot configuration achieved superior performance compared to standard CNN and Vision Transformer baselines. On the independent test set, the model attained an Accuracy of 0.9107, Sensitivity of 0.8603, and Specificity of 0.9324 for three-class severity stratification. Simultaneously, it achieved a Mean Absolute Error (MAE) of 8.2649 and a Mean Squared Error (MSE) of 151.4704, and an ${\mathrm{R}}^2$ of 0.7591 for FEV1/FVC ratio estimation. Qualitative analysis using saliency maps also suggested that the slot-based approach contributes to attention patterns that are more constrained to clinically relevant pulmonary structures. These results suggest that our slot-attention-based multi-task model offers a robust solution for automated COPD assessment from standard radiographs.

In this study, we propose a method of classification for tympanic membrane diseases and regression of pediatric hearing, using a deep learning model of artificial neural networks. Based on the B7 Backbone model of EfficientNet, a state-of-the-art convolutional neural network model, drop connect was applied in the encoder for generalization, and multi-layer perceptron, which is mainly used in the transformer, was applied to the decoder for improved accuracy. For the training data, the open-access tympanic membrane dataset, divided into four classes, was used as the benchmark dataset, and the SCH tympanic membrane dataset with five classes of tympanic membrane diseases and pediatric hearing was also used as the training dataset. In the benchmark using the open-access tympanic membrane dataset, the proposed model showed the highest performance among the five comparative models with an average accuracy of 93.59%, an average sensitivity of 87.19%, and an average specificity of 95.73%. In the experiment trained on the SCH tympanic membrane disease dataset, the average accuracy was 98.28%, the average sensitivity was 89.66%, the average specificity was 98.68%, and the average inference time was 0.2 s. In the experiment trained on the SCH pediatric hearing dataset, the mean absolute error was 6.8678, the mean squared logarithmic error was 0.2887, and the average inference time was 0.2 s.

In this paper, we propose a method to improve real-time performance and accuracy by applying Edge Information Block (EIB) to the existing SegNet to perform real-time image segmentation. The proposed method was used in an experiment to improve performance by 23.5% in the Camvid data and 6.6%p in the DeepCrack data. And as a result of comparing with existing real-time image segmentation models, the calculation speed was seen to be improved by 50 to 150%, but the accuracy was found to be 1 to 6%p different. Through this experiment, it was proved that edge information enhances real-time and accuracy.