Saurav Mallik

The paper introduces RSAUNet, a deep learning model for prostate cancer segmentation in MRI scans, achieving high Dice and Jaccard scores.

This paper proposes an explainable AI model for early glaucoma detection using a grey-wolf optimized extreme learning machine approach, achieving high accuracy on standard datasets.

This paper develops a PSO-optimized stacked ensemble machine learning model with SMOTEEN for stroke prediction, achieving 95.57% accuracy and outperforming traditional models.

This paper develops a deep learning model for cervical cancer classification using Pap smear images, achieving high accuracy and outperforming previous methods.

This paper evaluates filter-based feature selection methods on microarray datasets, finding that mRMR outperforms others in classification accuracy, F1-score, and RMSE.

This study uses LASSO regression and bioinformatics tools to identify prognostic genes in breast cancer, finding six chemokines associated with disease progression.