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p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Sathiyamurthy, Babu Kumar
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Automated lung cancer T-Stage detection and classification using improved U-Net model Sathiyamurthy, Babu Kumar; Madhaiyan, Vinoth Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6817-6826

Abstract

Lung cancer results from the uncontrolled growth of abnormal cells. This research proposes an automated, improved U-Net model for lung cancer detection and tumor staging using the TNM system. A novel mask-generation process using thresholding and morphological operations is developed for the U-Net segmentation process. In the pre-processing stage, an advanced augmentation technique and contrast limited adaptive histogram equalization (CLAHE) are implemented for image enhancement. The improved U-Net model, enhanced with an advanced residual network (ARESNET) and batch normalization, is trained to accurately segment the tumor region from lung computed tomography (CT) images. Geometrical parameters, including perimeter, area, convex area, solidity, roundness, and eccentricity, are used to find precise T-stage of lung cancer. Validation using performance metrics such as accuracy, specificity, sensitivity, precision, and recall shows the proposed hybrid method is more accurate than existing approaches, achieving a staging accuracy of 94%. This model addresses the need for a highly accurate automated technique for lung cancer staging, essential for effective detection and treatment.
Optimizing citrus disease detection: a transferrable convolutional neural network model enhanced with the fruitfly optimization algorithm Ganadalu Lingaraju, Anoop; Mangala Shankaregowda, Asha; Sathiyamurthy, Babu Kumar; Budanoor Channegowda, Santhrupth; Jalapur, Shruti; Palahalli Chennakeshava, Chaitra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3201-3213

Abstract

Fungal, bacterial, and viral diseases significantly threaten citrus production and quality worldwide, prompting producers to explore technological solutions to mitigate the financial impact of these diseases. Image analysis techniques have emerged as powerful tools for detecting citrus diseases by differentiating between healthy and diseased specimens through the extraction of discriminative features from input images. This paper introduces a valuable dataset comprising 953 color images of orange leaves from the species Citrus sinensis (L.) Osbeck, which serves to train, evaluate, and compare various algorithms aimed at identifying abnormalities in citrus fruits. The development of automated detection systems is crucial for reducing economic losses in citrus production, with this research focusing on twelve specific diseases and nutrient deficiencies. We propose a novel approach to citrus plant disease detection utilizing a hyper-parameter tuned transferrable convolutional neural network (TCNN) model, referred to as the enhanced fruitfly optimization algorithm (EFOA)-TCNN model. This model optimizes the parameters of TCNN using the EFOA and enhances architectural design by incorporating three convolutional layers alongside an energy layer instead of a traditional pooling layer. Experimental results demonstrate that the proposed EFOA-TCNN model outperforms existing state-of-the-art methods, achieving a sensitivity of 0.975 and an accuracy of 0.995.
Co-Authors Budanoor Channegowda, Santhrupth Ganadalu Lingaraju, Anoop Jalapur, Shruti Madhaiyan, Vinoth Kumar Mangala Shankaregowda, Asha Palahalli Chennakeshava, Chaitra