Amitava Halder

About

Name: Dr. Amitava Halder.
Highest Educational Qualification: PhD in Engineering, Jadavpur University, Kolkata, India.
Research Area: Medical Imaging, Signal Processing, Image Processing, Machine Learning, Deep Learning, Computer Vision.
Personal Email Id: [email protected]

Researchers and Community members are hereby requested to mail me directly over my personal email id ([email protected]) only for any collaborative project/research work/queries/communication.

Employment

• Dr. Sudhir Chandra Sur Institute of Technology and Sports Complex Assistant Professor

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (12)

• An attention aided wavelet convolutional neural network for lung nodule characterization
  International Journal of Medical Informatics 2026 DOI: 10.1016/j.ijmedinf.2025.106118
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• A Novel PHOG Feature for Lung Nodule Characterization
  2025 International Conference on Computing, Intelligence, and Application (CIACON) 2025 DOI: 10.1109/ciacon65473.2025.11189582
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• An Efficient Nodule Characterization Framework using Gabor Filter and Shape Features
  2025 International Conference on Computer, Electrical & Communication Engineering (ICCECE) 2025
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• Studies on some image processing and deep learning algorithms for computer-aided diagnosis system in detection, characterization, and subtype classification of lung cancer
  2024
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• MorphAttnNet: An Attention-based morphology framework for lung cancer subtype classification
  Biomedical Signal Processing and Control 2023 DOI: 10.1016/j.bspc.2023.105149
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• Atrous convolution aided integrated framework for lung nodule segmentation and classification
  Biomedical Signal Processing and Control 2023 DOI: 10.1016/j.bspc.2022.104527
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• Adaptive morphology aided 2-pathway convolutional neural network for lung nodule classification
  Biomedical Signal Processing and Control 2022 DOI: 10.1016/j.bspc.2021.103347
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• Morphological Filter Aided GMM Technique for Lung Nodule Detection
  2020 IEEE Applied Signal Processing Conference (ASPCON) 2020 DOI: 10.1109/aspcon49795.2020.9276715
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• Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review
  Journal of Digital Imaging 2020 DOI: 10.1007/s10278-020-00320-6
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• Superpixel and Density Based Region Segmentation Algorithm for Lung Nodule Detection
  2020 IEEE Calcutta Conference (CALCON) 2020 DOI: 10.1109/calcon49167.2020.9106569
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