raihan islam

Abstract Accurate and efficient identification of cucumber leaf diseases is a critical step in preventing losses and facilitating timely intervention in agricultural activi-ties. However, most state-of-the-art plant disease recognition models, including those employing deep learning, often fail to identify spatial dependencies among symptomatic leaf feature regions, require high computational resources, and lack robustness in their predictions.

Abstract Accurate recognition of flower growth stages is important for plant phenotyping but remains challenging due to subtle visual differences and limited labeled data. This study proposes a hybrid CNN/Transformer + GCN framework for fine-grained flower growth-stage classification. A new dataset, BD Flower Growth, is introduced with 3,889 original images from eight Bangladeshi flower species, categorized into three stages (early, mid, full), forming 24 classes.

Abstract Cytopathology, often abbreviated as cytology, has a central role in the early detection of cancer, such as cervical, lung and bladder cancers, owing to its speed, simplicity and minimally invasive nature1,2,3,4,5,6,7,8,9. However, its effectiveness is limited by variability in diagnostic accuracy stemming from subjective visual interpretation10,11,12,13,14,15,16,17,18,19,20,21.