Agnesh Chandra Yadav

About

I am Agnesh Chandra Yadav. My academic journey began with a B.Tech in Electronics and Communication Engineering at the esteemed National Institute of Technology (NIT) Patna, where I graduated in 2019. Subsequently, I pursued an M.Tech in Digital Communication Engineering at the Maulana Azad National Institute of Technology (MANIT) Bhopal, completing my degree in 2022. Currently, I am immersed in the exciting and intellectually stimulating environment of the Indian Institute of Technology (IIT) Patna, where I am pursuing a Ph.D. In the intersection of image processing and medical image analysis, I focus on enhancing low-light images and contributing to the classification and segmentation of medical images, particularly in the realms of brain tumor and breast cancer diagnosis. My commitment to advancing knowledge in these areas is evident through my membership in relevant professional organizations. I am currently focused on my Ph.D. research, aiming to contribute meaningfully to the field and address critical challenges in healthcare diagnostics.

Employment

Employment history is unavailable.

Education

• Indian Institute of Technology Patna PhD
  2022 - 2026
• Maulana Azad National Institute of Technology M.Tech
  2020 - 2022
• National Institute of Technology Patna B.Tech
  2015 - 2019

Projects & Funding

Projects & funding information is unavailable.

Publications (14)

• Deep feature-based approaches for brain tumor classification and segmentation in medical imaging
  Biomedical Signal Processing and Control 2026 DOI: 10.1016/j.bspc.2026.109603
  +Save
• DARF-Net: A Dual Attention Retinex-Based Fusion Network for Low-Light Image Enhancement
  IEEE Sensors Letters 2026 DOI: 10.1109/LSENS.2026.3651748
  +Save
• Modified Recurrent Residual Attention U-Net model for MRI-based brain tumor segmentation
  Biomedical Signal Processing and Control 2025 DOI: 10.1016/j.bspc.2024.107220
  +Save
• Computer-aided diagnosis for multi-class classification of brain tumors using CNN features via transfer-learning
  Multimedia Tools and Applications 2025 DOI: 10.1007/s11042-025-20751-z
  +Save
• Image informatics for clinical and preclinical biomedical analysis
  Mining Biomedical Text, Images and Visual Features for Information Retrieval 2025 DOI: 10.1016/b978-0-443-15452-2.00020-0
  +Save
• DCRUNet++: A Depthwise Convolutional Residual UNet++ Model for Brain Tumor Segmentation
  Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 2025 DOI: 10.1007/978-3-031-78398-2_18
  +Save
• AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation
  2024 4th International Conference on Advancement in Electronics and Communication Engineering Aece 2024 2024 DOI: 10.1109/AECE62803.2024.10911235
  +Save
• EffUNet++: A Novel Architecture for Brain Tumor Segmentation Using FLAIR MRI Images
  IEEE Access 2024 DOI: 10.1109/ACCESS.2024.3480271
  +Save
• Image Enhancement by Defogging utilizing Scene Specific Dark Channel Prior
  2024 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2024 2024 DOI: 10.1109/SCEECS61402.2024.10482018
  +Save
• Image informatics for clinical and preclinical biomedical analysis
  Mining Biomedical Text Images and Visual Features for Information Retrieval 2024 DOI: 10.1016/B978-0-443-15452-2.00020-0
  +Save