Identification of Lacerations Caused by Cervical Cancer through a Comparative Study among Texture-Extraction Techniques
Source abstract
Abstract
Cervical cancer is a disease affecting a worrisomely large number of women worldwide. If not treated in a timely fashion, this disease can lead to death. Due to this problematic, this research employed the LBP, OC_LBP, CS-LTP, ICS-TS, and CCR texture descriptors for the characteristic extractions of 60 selected carcinogenic images classified as Types 1, 2, and 3 according to a database; afterward, a statistical multi-class classifier and an NN were used for image classification. The resulting characteristic vectors of all five descriptors were implemented in four tests to identify the images by type. The statistical multi-class combination and classification of all images achieved a classification efficiency of 83–100%. On the other hand, with the NN, the LBP, OC_LBP, and CCR descriptors presented a classification efficiency of between 81.6 and 98.3%, differing from that of ICS_TS and CS_LTP, which ranged from 36.6 to 55%. Based on the tests performed with regard to ablation, ROC curves, and confusion matrix, we consider that an efficient expert system can be developed with the objective of detecting cervical cancer at early stages.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departamento de Ciencias Básicas, Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán 47810, Jalisco, Mexico
Departamento de Electro-Fotónica, Centro Universitario de Ciencias Exactas e Ingeniería, Universidad de Guadalajara, Guadalajara 44430, Jalisco, Mexico
References (55)
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