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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Robotics and Control (JRC)
Govindaraju, Kalpana
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Unveiling the Advancements: YOLOv7 vs YOLOv8 in Pulmonary Carcinoma Detection Elavarasu, Moulieswaran; Govindaraju, Kalpana
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20900

Abstract

In this work, precision and recall measures are used to assess the performance of YOLOv7 and YOLOv8 models in identifying pulmonary carcinoma on a distinct collection of 700 photos. The necessity of early disease detection is increasing, thus choosing a reliable object detection model is essential. The goal of the research is to determine which model works best for this purpose, taking into account the unique difficulties that pulmonary cancer presents. The work makes a contribution to the field by showcasing the improvements made to YOLOv8 and underlining how well it detects both benign and malignant. YOLOv7 and YOLOv8 were used to independently train custom models using the pulmonary carcinoma dataset. The models' performance was measured using precision, recall, and mean average precision measures, which allowed for a comprehensive comparison examination. When it came to precision (58.2%), recall (61.2%), and mean average precision at both the 0.5:0.95 (33.3%) and 0.5 (53.3%) criteria, YOLOv8 outperformed YOLOv7. The 3.0% accuracy gain highlights YOLOv8's improved capabilities, especially in identifying small objects. YOLOv8's enhanced accuracy can be attributed to the optimisation of the detection process through its anchor-free design. According to this study, YOLOv8 is a more reliable model for pulmonary carcinoma identification than YOLOv7. The results indicate that YOLOv8 is the better option because of its higher recall, precision, and enhanced capacity to detect smaller objects—all of which are critical for early illness detection in medical imaging.
Effectiveness of filtering methods in enhancing pulmonary carcinoma image quality: a comparative analysis Elavarasu, Moulieswaran; Govindaraju, Kalpana
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp358-365

Abstract

In recent years, information technology has vastly improved. The quality of the image has been degraded by noise, which defeats the purpose of the noisy images. The major purpose of this paper is to find out which filters provide a better outcome while preprocessing medical images using computer tomography scans. The purpose of this paper is to remove noise from any images, whether they are real-time datasets or online datasets. To enhance an image for preprocessing, we have compared various filters; these filters are already available, but the major purpose is to identify the best filter. We compared the different parameters to find the best and finally found that the modified bilateral filtering provided a better result. The noise has been removed by using a bilateral filter, and the image clarity has not changed when using this filter. We have discussed the advantages and drawbacks of each approach. The effectiveness of these filters is compared using the peak signal-to-noise ratio, structural similarity index, contrast-to-noise ratio, and mean square error. The proposed algorithm is tested on 5 sample lung images. The results show that the modified bilateral filter produces better results.
Optimizing pulmonary carcinoma detection through image segmentation using evolutionary algorithms Elavarasu, Moulieswaran; Govindaraju, Kalpana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2912-2922

Abstract

This paper’s goal is to suggest an image segmentation technique for use with medical images, specifically computer tomography scan images, to aid doctors in understanding the images. To address a variety of picture segmentation issues, it is necessary to investigate and apply novel evolutionary algorithms. The study focuses on pulmonary carcinoma, which is the cancer that affects males the most frequently across the globe. For proper treatment and life-saving measures, early identification of lung cancer is essential. To identify lung cancer, doctors frequently employ the computed tomography imaging technique. In order to extract tumours from lung scans, the study analyses the effectiveness of three optimization algorithms: k-means clustering, particle swarm optimization, and modified guaranteed convergence particle swarm optimization. The study also examines the pre-processing performance of four filters, namely the mean, bilateral, gaussian, and laplacian filters, shows that the bilateral filter is best suited for CT scans of the body. To test the proposed technique on 30 examples of lung scans. The proposed algorithm is tested on 30 sample lung images. The results show that the modified guaranteed convergence particle swarm optimization algorithm has the highest accuracy of 96.01%.
Co-Authors Elavarasu, Moulieswaran