Santhi, Ramalingam Benign and Malignant Pattern Identification in Cytopathological Images of Thyroid Nodules using Gabor Filter and Neural Networks. Asian Journal of Convergence in Technology, IV (I). ISSN 2350-1146

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Abstract

— This research work presents an automated pattern
recognition system to discriminate benign and malignant thyroid
nodules using Gabor features based Neural Network classifiers.
In the preprocessing step, the required regions of multi-stained
Fine Needle Aspiration Cytology images of thyroid nodules are
automatically cropped. Then, the segmentation of foreground
information is performed using mathematical morphology
technique. In the post processing step, the significant statistical
features are extracted from the segmented images with the help
of Gabor filter under various frequencies and orientations.
Finally, the benign and malignant image patterns are
discriminated using Elman Neural Network and Auto-associative
Neural Network. Based on the performance analysis, the
discrimination accuracy of 93.33% is obtained by the Elman
Neural Network classifier for the statistical features extracted by
Gabor filter bank. However, the auto-associative Neural Network
classifier reports a highest discrimination accuracy of 96.66% for
the same configuration. This automated discrimination system
can be used as a second opinion tool for thyroid nodule analysis
by the pathologists.

Item Type: Article
Uncontrolled Keywords: Cytology; Fine Needle Aspiration; Gabor Filter; Morphology; Neural Network; Segmentation
Depositing User: Mr Team Mosys
Date Deposited: 18 Aug 2022 04:39
Last Modified: 18 Aug 2022 04:39
URI: http://ir.psgcas.ac.in/id/eprint/1488

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