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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

A brain tumor is an asymmetrical expansion by cells inevitably emulating amid them. Image processing is a vibrant research area where the handing out of the image in the medical field is an exceedingly tricky field. In this paper, an expert algorithm is suggested for the detection of pituitary brain tumors from MR images.

Methods

The preprocessing techniques (smoothing, edge detection, filtering) and segmentation techniques (watershed) are applied to the online data set. The transfer learning technique is used as a classifier whose performance is measured in terms of classification accuracy. Resnet 50, Inception V3VGG16, and VGG19 models are used as classification algorithms. The proposed model is validated using different machine learning techniques considering hybrid features.

Results

96% accuracy was obtained employing the Inception V3 model & 95% accuracy was attained using hybrid GLDS and GLCM features employing Support Vector Machine algorithm while 93% was attained using Probabilistic Neural Network and k Nearest Neighbor techniques.

Conclusion

Computer-aided systems gave much faster and more accurate results than image processing techniques.1.0% accuracy improvement was observed while using Inception V3 over GLDS + GLCM + SVM and 2.1% accuracy improvement using GLDS + GLCM + SVM over GLDS + GLCM + kNN.

© 2024 The Author(s). Published by Bentham Open.
© 2024 The Author(s). Published by Bentham Open. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2024-01-01
2024-11-22

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CAD System Design for Pituitary Tumor Classification based on Transfer Learning Technique
Curr. Med. Imaging 20, E15734056246146 (2024); https://doi.org/10.2174/0115734056246146231018110415
/content/journals/cmir/10.2174/0115734056246146231018110415
/content/journals/cmir/10.2174/0115734056246146231018110415
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  • Article Type:     Research Article
Keyword(s): Classification; Pituitary tumor; Pre-processing; Support vector machine; Transfer learning

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