Classification System for Cervical Cell Images based on Hu Moment Invariants Methods and Support Vector Machine

Sari, Brilian Permata; Jusman, Yessi

doi:10.1109/conit51480.2021.9498353

Cited by 11 publications

(1 citation statement)

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“…The HU invariant moment is a method used to obtain the characteristics of digital objects, such as position, axis rotation, and changes in scale, reducing errors in recognition or identification [ 48 ]. In [ 49 ], seven functions were defined as nonlinear representation at regular moments that are invariant to rotation, scale, and translation.…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques

Astolfi

Silva

Guedes

et al. 2023

Sensors

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Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS–Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.

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Section: Methodsmentioning

confidence: 99%