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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Soubhagya Sankar Barpanda
VIT-AP University
Electrical & Electronics Engineering

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Hyperspectral image classification using Hyb-3D convolution neural network spectral partitioning Easala Ravi Kondal; Soubhagya Sankar Barpanda
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp295-303

Abstract

Hyperspectral image classification (HSIC) on remote sensing imaging has brought immersive achievement using artificial intelligence technology. In deep learning convolution neural networks (CNN), 2D-CNN, and 3D-CNN methods are widely used to classify the spectral-spatial bands of hyperspectral images (HSI). The proposed Hybrid 3D-CNN (H3D-CNN) model framework for deeper features extraction predicts classification accuracy in supervised learning. The model reduces the narrow gap between supervised and unsupervised learning and the complexity and cost of the previous models. The HSI classification analysis is carried out on real-world data sets of Indian pines Salinas datasets captured by Airborne visible, infrared imaging spectrometer (AVIRIS) sensors that performed superior classification accuracy results.
Curriculum learning based overcomplete U-Net for liver tumor segmentation from computed tomography images Bindu Madhavi Tummala; Soubhagya Sankar Barpanda
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4676

Abstract

In this paper, we have proposed an overcomplete U-Net to perform liver tumor segmentation jointly using a curriculum learning strategy. Liver tumor segmentation is the most prominent and primary step in treating liver cancer and can also help doctors with proper diagnosis and therapy planning. However, it is challenging because of variations in shape, position, and depth of tumors and adjacent boundaries with internal organs around the liver. We have presented a promising solution by designing a U-Net-based segmentation network with two branches: an overcomplete branch to fine grade the small structures and an undercomplete branch to fine grade the high-level structures. This combination allows the network to learn all types of tumor artifacts more accurately. We also changed the conventional learning paradigm to curriculum learning where the input images are fed to the network from easy to hard ones to achieve faster convergence. Finally, our network segments the tumors directly from the whole medical images without the need for segmented liver region of interests (ROIs). The proposed network achieved a DICE score of 75% in tumor segmentation which is a decent value when compared with some existing deep learning methods for liver tumor segmentation.
Glaucoma classification using a polynomial-driven deep learning approach Krishna Santosh Naidana; Soubhagya Sankar Barpanda
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4670

Abstract

In this paper, a deep learning-based multi-stage polynomial driven glaucoma classification-net (PDGC-Net) has been proposed for glaucoma identification through retinal images. The proposed approach begins with retinal image pu[1]rification by noise estimation and reduction. Noise has been estimated using a polynomial coefficient-based approach. Images are classified using PDGC-Net, whose polynomial indeterminate representative blocks are designed using new convolutional neural networks (CNN) architectures. The performance of PDGC[1]Net has been observed on the ACRIMA, ORIGA, and retinal image database for optic nerve evaluation (RIM-ONE) datasets. The experimentation is carried out on noisy and denoised images separately, and PDGC-Net has achieved 96% to 98% and 98% to 100% accuracy ranges, respectively. The model’s elasticity is tested with various stages of PDGC-Net. The quantitative PDGC-Net perfor[1]mance analysis is done with state-of-the-art CNN models. The proposed model’s performance has been proven and could be an effective aid to ophthalmologists for glaucoma screening (GS).
Co-Authors Bindu Madhavi Tummala Easala Ravi Kondal Krishna Santosh Naidana