Scientific Reports, Published online: 24 December 2025; doi:10.1038/s41598-025-32607-5Deep learning-based disease detection in potato and mango leaves: a comparative study of CNN, AlexNet, ResNet, and EfficientNet

Researchers have developed a streamlined artificial intelligence system, named KD-OCT, that accurately diagnoses and classifies retinal diseases causing vision loss, while significantly reducing the computational demands needed for real-time deployment in clinical settings.

AI의 역사는 '과장된 약속 → 혹독한 겨울 → 하드웨어·데이터·알고리즘 삼박자의 혁신'이 반복된 이야기예요. 지금의 Ch

Figure 1 shows the architecture proposed DL framework for infectious disease classification using cough sounds. Fig. 1 Overall block diagram of the proposed deep learning framework for infectious disease classification using cough sounds. Full size image In this work, a...

Deep learning offers promising potential for automating the interpretation of immunoglobulin A (IgA) endomysial antibody (EMA) tests, a critical serological test for the diagnosis of celiac disease that currently requires labor-intensive and subjective human interpretation. In this study, we employ and comprehensively evaluate the performance of the EfficientNet and EfficientNetV2 architectures in binary (positive vs negative, where all weak and strong positive signals were grouped as positive), three-class (negative, weak positive, strong positive), and four-class (negative, weak positive, strong positive and gray zone) classification scenarios using immunofluorescence images of IgA EMA equivalent (EMA-eq) tests. Our experiments on 368 clinical samples show high performance, with EfficientNetV2-S achieving an accuracy of 99.37% in binary classification, 95.28% in three-class classification, and 86.98% in the complex four-class scenario that introduces gray zone cases as a distinct interpretive category. Contrary to conventional assumptions, medium-sized deep architectures consistently outperformed their larger counterparts. The superior performance of the EfficientNet-V2 models can be attributed to their architectural innovations and higher input resolution (640 × 640 pixels), which proved critical for capturing subtle immunofluorescence patterns. We also incorporate HiRes-CAM (Class Activation Mapping), a convolutional neural network oriented visual explanation tool, to better understand the decisions of the underlying trained deep learning model in an explainable artificial intelligence (AI) manner. This study demonstrates that deep learning has the potential to achieve expert-level performance in EMA-eq test interpretation, offering a path toward more standardized, efficient and objective celiac disease diagnosis while reducing the burden on specialist medical staff.