Rice is the primary staple food in many Asian countries, and ensuring the quality of rice crops is vital for food security. Effective crop management depends on the early and precise detection of common rice diseases such as bacterial blight, blast, brown spot, and tungro. This work presents a convolutional neural network model for classifying rice leaf disease. Four distinct diseases, bacterial blight, blast, brown spot, and tungro, are the main targets of the model. Previously, leaf pathologies in crops were mostly identified manually using specialized equipment, which was time-consuming and inefficient. This study offers a remedy for accurately diagnosing and classifying rice leaf diseases through deep learning techniques. Using this dataset, the proposed CNN model was trained to identify complex patterns and attributes linked to each disease using its deep learning capabilities. This CNN model achieved an exceptional accuracy of 99.99%, surpassing the benchmarks set by existing state-of-the-art models. The proposed model can be a useful diagnostic and early warning system for rice leaf diseases. It could help farmers and other agricultural professionals reduce crop losses and enhance the quality of their yields.

The perception of animal emotions is key to enhancing veterinary practice, human–animal interactions, and protecting domesticated species’ welfare. This study presents a unique emotion classification deep learning-based approach for pet animals. The actual and emotional status of dogs and cats have been classified using a modified EfficientNetB5 model. Utilizing a dataset of images classified into four different emotion categories—angry, sad, happy, and neutral—the model incorporates sophisticated feature extraction methods, such as Dense Residual Blocks and Squeeze-and-Excitation (SE) blocks, to improve the focus on important emotional indicators. The basis of the second strategy is EfficientNetB5, which is known for providing an optimal balance in terms of accuracy and processing capabilities. The model exhibited robust generalization abilities for the subtle identification of emotional states, achieving 98.2% accuracy in training and 91.24% during validation on a separate dataset. These encouraging outcomes support the model’s promise for real-time emotion detection applications and demonstrate its adaptability for wider application in ongoing pet monitoring systems. The dataset will be enlarged, model performance will be enhanced for more species, and real-time capabilities will be developed for real-world implementation.

Haider Ali, Abdul Rehman Khalid, Ijaz Muhammad Umer, and Hee-Cheol Kim. Journal of information and communication convergence engineering 2025;23:135-43. https://doi.org/10.56977/jicce.2025.23.2.135

International Classification of Diseases (ICD) codes enable correct billing, insurance reimbursement, and healthcare analytics. However, manual coding is time-consuming, expensive, and error-prone, creating bottlenecks in clinical workflow and limiting scalability. Artificial intelligence (AI) has emerged as a promising solution for automated ICD code assignment from unstructured clinical text. This systematic review explores the current state of automated ICD coding research, examining models applied to diverse clinical documents including discharge summaries, electronic health records, nursing notes, and pathology reports. Following PRISMA guidelines, we searched six databases for studies published between 2019 and 2024, selecting 54 relevant studies from 4,280 initial citations. Our analysis reveals the use of diverse datasets, preprocessing techniques, and feature extraction methods, alongside a clear evolution from traditional machine learning to deep learning approaches, with substantial architectural diversity across convolutional, recurrent, transformer, and hybrid models. Performance varies considerably across dataset configurations, with models achieving higher accuracy on frequent code subsets compared to full label spaces. However, critical gaps persist: overreliance on single-language, single-institution datasets limits generalizability; difficulties in predicting rare codes remain unresolved; lack of model interpretability undermines clinical trust; and inconsistent evaluation protocols hinder meaningful comparison. To address these challenges, we propose a 5P evidence-grounded research agenda: Population Diversity, Performance Robustness, Prediction of Rare Codes, Provenance Transparency, and Practical Integration. These findings underscore AI’s potential to transform ICD coding while highlighting the need for standardized benchmarks, rigorous external validation, multilingual datasets, and explainable architectures to enable equitable and effective deployment in real-world healthcare systems.

Segmentation of nuclei in histopathology images stained with Hematoxylin and Eosin is essential for accurate disease diagnosis and tissue characterization. Traditional methods usually struggle with complicated tissue structures and overlapping nuclei. Deep learning-based approaches, particularly U-Net along with its variants, have demonstrated improved effectiveness but still face limitations, including restricted receptive fields, overfitting, boundary ambiguity, and uneven nuclei distribution. This paper presents the deep image segmentation network (DISegNet) customized for nuclei segmentation. DISegNet integrates enhanced lightweight convolutional modules with expanded receptive fields and introduces an Improved Global Context (IGC) block to capture long-range dependencies. A dedicated stem block further boosts feature extraction. These components improve boundary precision and model robustness in visually challenging conditions. We examine DISegNet on the MoNuSAC dataset, its subsets, and the MoNuSeg dataset. Experimental results show that DISegNet consistently outperforms preexisting fully supervised segmentation methods. Further, in a bid to mitigate the costly dense annotations, we introduce a weakly supervised approach using refined-point annotations. Point annotations are transformed to coarse labels via k-means clustering and Voronoi diagrams. This strategy significantly reduces annotation effort while maintaining high segmentation performance, yielding more efficient and accurate results than previous point annotation approaches. Future work will explore our models for broader medical imaging tasks involving intricate anatomical structures and overlapping regions.