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Dr Victor Ikechukwu Agughasi

vidwan id: 169687
Male

Assistant Professor, Department of Computer Science and Engineering
Maharaja Institute of Technology, Mysore

Expertise

  • Computer Science Artificial Intelligence

Publications

Total Articles 15
Books 0
Proceedings 0

Publications

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Scopus

Citations 78
h-index 5

CrossRef

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Citations 122
h-index 4
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Professional Recognition

2016

Gold Medalist and Best Graduating Student of 2016 Batch

Bangalore University

Community & Membership

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Bio

Medical Imaging and Machine Learning

Personal Details

  • Male
  • Assistant Professor , Maharaja Institute of Technology, Mysore
  • Department of Computer Science and Engineering, Maharaja Institute of Technology
Ph.D
University of Mysore 2024
MSc
Bangalore University 2016
Assistant Professor Oct 2023 – Present
Maharaja Institute of Technology, Mysore | Department of Computer Science and Engineering

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Co-Authors (3)

Deepu

Dr Deepu R

Maharaja Institute of Technology, Mysore

Murali

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Scholarly Work

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Scholarly Publications

COPDNet: An Explainable ResNet50 Model for the Diagnosis of COPD from CXR Images

Open Access
conference proceedings
Authors: Agughasi Victor Ikechukwu, Murali S

Semi-supervised labelling of chest x-ray images using unsupervised clustering for ground-truth generation

Open Access
article
Authors: Victor Ikechukwu Agughasi ., Murali Srinivasiah .,

XAI: An Explainable AI Model for the Diagnosis of COPD from CXR Images

Open Access
Conference Paper
Authors: Ikechukwu A.V.;Murali S.

COPDNet: An Explainable ResNet50 Model for the Diagnosis of COPD from CXR Images

Open Access
Conference Paper
Authors: Ikechukwu A.V.;Murali S.;Honnaraju B.

i-Net: a deep CNN model for white blood cancer segmentation and classification

Open Access
Article

ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images

Open Access
conference proceedings
Authors: A. Victor Ikechukwu ., S. Murali ., R. Deepu ., R.C. Shivamurthy .,

Early Prognosis of Heart Failure from Clinical Symptoms using K-Means and Naïve Bayes Algorithms

Open Access
article
Authors: Agughasi Victor Ikechukwu, DK Yashashwini, Das M Snehil