All Outputs (2)

Enhancing brain tumor classification with transfer learning across multiple classes: an in-depth analysis. (2023)
Journal Article
AHMMED, S., PODDER, P., MONDAL, M.R.H., RAHMAN, S.M.A., KANNAN, S., HASAN, M.J., ROHAN, A. and PROSVIRIN, A.E. 2023. Enhancing brain tumor classification with transfer learning across multiple classes: an in-depth analysis. Biomedinformatics [online], 3(4), pages 1124-1144. Available from: https://doi.org/10.3390/biomedinformatics3040068

This study focuses on leveraging data-driven techniques to diagnose brain tumors through magnetic resonance imaging (MRI) images. Utilizing the rule of deep learning (DL), we introduce and fine-tune two robust frameworks, ResNet 50 and Inception V3,... Read More about Enhancing brain tumor classification with transfer learning across multiple classes: an in-depth analysis..

Rethinking densely connected convolutional networks for diagnosing infectious diseases. (2023)
Journal Article
PODDER, P., ALAM, F.B., MONDAL, M.R.H., HASAN, M.J., ROHAN, A. and BHARATI, S. 2023. Rethinking densely connected convolutional networks for diagnosing infectious diseases. Computers [online], 12(5), article 95. Available from: https://doi.org/10.3390/computers12050095

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. I... Read More about Rethinking densely connected convolutional networks for diagnosing infectious diseases..