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PortNet: Achieving lightweight architecture and high accuracy in lung cancer cell classification.
Heliyon
February 2025
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
Background: As one of the cancers with the highest incidence and mortality rates worldwide, the timeliness and accuracy of cell type diagnosis in lung cancer are crucial for patients' treatment decisions. This study aims to develop a novel deep learning model to provide efficient, accurate, and cost-effective auxiliary diagnosis for the pathological types of lung cancer cells.
Method: This paper introduces a model named PortNet, designed to significantly reduce the model's parameter size and achieve lightweight characteristics without compromising classification accuracy.
Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections.
Front Plant Sci
January 2025
Department of International Business Management, Tashkent State University of Economics, Tashkent, Uzbekistan.
This study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved residual skip connections. In light of increasing global challenges related to food security and sustainable agriculture, this research focuses on developing a highly efficient and accurate automated system for identifying plant diseases, thereby contributing to enhanced crop protection and yield optimization. The proposed model is trained on a comprehensive dataset encompassing various plant species and disease categories, ensuring robust performance and adaptability.
View Article and Find Full Text PDFEvaluation of the practical application of the category-imbalanced myeloid cell classification model.
PLoS One
January 2025
School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.
The incidence of acute myeloid leukemia (AML) is increasing annually, and timely diagnostic and treatments can substantially improve patient survival rates. AML typing traditionally relies on manual microscopy for classifying and counting myeloid cells, which is time-consuming, laborious, and subjective. Therefore, developing a reliable automated model for myeloid cell classification is imperative.
View Article and Find Full Text PDFDeep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection.
Front Neuroinform
January 2025
Department of Computer Science and Engineering, Institute of Technology, Nirma University, Gujarat, India.
Introduction: The prevalence of age-related brain issues has risen in developed countries because of changes in lifestyle. Alzheimer's disease leads to a rapid and irreversible decline in cognitive abilities by damaging memory cells.
Methods: A ResNet-18-based system is proposed, integrating Depth Convolution with a Squeeze and Excitation (SE) block to minimize tuning parameters.
Climate change and the cost-of-living squeeze in desert lizards.
Science
January 2025
School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia.
Climate warming can induce a cost-of-living "squeeze" in ectotherms by increasing energetic expenditures while reducing foraging gains. We used biophysical models (validated by 2685 field observations) to test this hypothesis for 10 ecologically diverse lizards in African and Australian deserts. Historical warming (1950-2020) has been more intense in Africa than in Australia, translating to an energetic squeeze for African diurnal species.
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