
International Journal on Science and Technology
E-ISSN: 2229-7677
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Impact Factor: 9.88
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 16 Issue 2
April-June 2025
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Image Processing Using Machine Learning
Author(s) | Aishwarya Patil, Dr. Priyanka Singh |
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Country | India |
Abstract | Image in the field of image processing we have seen great growth with the addition of machine learning which in turn presents more efficient, accurate, and automated solutions for many fields. In past we saw that traditional image processing had issues with noise, low contrast, and manual feature extraction which machine learning has been very successful in solving. This work we present an overview of today’s image processing which we see improved by machine learning algorithms especially in the case of Convolutional Neural Networks for feature extraction, image segmentation, classification and object detection. We report on our studies which used VOC2007, ImageNet, and CIFAR100 datasets to prove that machine learning based methods we see perform very well with an accuracy of 98.4% for image segmentation, 98.7% for classification, and 98.6% for target detection. TI mage processing has seen great transformation with the introduction of machine learning which also brings to the table better, more accurate. |
Keywords | Image Processing, Machine Learning, Deep Learning, CNN, Image Segmentation, Classification, Object Detection, Feature Extraction, Preprocessing, Model Training, Transfer Learning, Medical Imaging, Diabetic Retinopathy Detection, Autonomous Driving, Data Augmentation, Performance Evaluation, Accuracy, Precision, Recall, Robustness, Real-time Processing, Computational Requirements, Model Interpretability, Ethical Issues, Dataset Challenges, AI, SVM, KNN, GANs. |
Field | Computer Applications |
Published In | Volume 16, Issue 2, April-June 2025 |
Published On | 2025-06-20 |
DOI | https://doi.org/10.71097/IJSAT.v16.i2.6270 |
Short DOI | https://doi.org/g9qxd8 |
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IJSAT DOI prefix is
10.71097/IJSAT
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