Related Experiment Video For DarkNet53 model
Updated: May 5, 2026
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
Published on: April 8, 2016
Prostate Cancer Classification and Interpretation With Multiparametric Magnetic Resonance Imaging and Gleason Grade
Vasantha Pragasam Gladis Pushparathi1, Dhas Justin Xavier2, Pandian Chitra3
1Department of Computer Science and Engineering, Velammal Institute of Technology, Panchetti, Chennai, Tamil Nadu, India.
Background:
Prostate Cancer (PCa) increases the mortality rate of males worldwide and is caused by genetics, lifestyle, and age reasons. The existing automated PCa classification systems face difficulties with overfitting issues, and non-generalizability, leading to poor classification performance.
Objective:
On this account, this study proposes an automated classification of PCa from MRI images using a hybrid weighted mean of vectors-optimized DarkNet53 classifier model.
Methodology:
The proposed method suggests nonlocal mean filtering for noise reduction, N4ITK bias field correction to enhance image quality, and active contour-based segmentation for accurately identifying the disease region. The feature extraction utilizes the gray level run length matrix and shape features for effective feature extraction. A weighted mean of vectors optimization is used to optimize the feature selection process by hybridizing it with the DarkNet53 model for classification. Finally, the interpretation of achieving the classification has been demonstrated using the explainable AI Grad-CAM model.
Results:
After comparing the proposed work with various state-of-the-art algorithms, the proposed model achieves 99.31% accuracy, 98.24% sensitivity, and 98.46% specificity, respectively, highlighting the model's accomplishment using the DarkNet53 classifier.
