DenseNet201Plus: Cost-effective transfer-learning architecture for rapid leaf disease identification with attention mechanisms.

Plant leaf diseases are a significant concern in agriculture due to their detrimental impact on crop productivity and food security. Effective disease management depends on the early and accurate detection and diagnosis of these conditions, facilitating timely intervention and mitigation strategies. In this study, we address the pressing need for accurate and efficient methods for detecting leaf diseases by introducing a new architecture called DenseNet201Plus.

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images.

Leaf diseases are a global threat to crop production and food preservation. Detecting these diseases is crucial for effective management. We introduce LeafDoc-Net, a robust, lightweight transfer-learning architecture for accurately detecting leaf diseases across multiple plant species, even with limited image data.

Bardet-Biedl syndrome: Delayed diagnosis in a 14-year-old child with end-stage renal disease.

Bardet-Biedl syndrome (BBS) is a rare autosomal recessive ciliopathic disorder. Because of its low prevalence and wide spectrum of clinical features, many patients remain undiagnosed. We report a case of a 14-year-old boy with a typical phenotype of BBS who remains undiagnosed until the development of end-stage renal disease.