Analysis on Various Feature Extraction Methods for Medical Image Classification

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

“…In another framework that is infrequently considered, given the flexibility of AI, the raw outputs may be utilized directly as inputs of deep learning models such as autoencoders and convolutional neural networks as well as traditional feature extraction methods to allow the algorithms to use features (or predictors) extracted either automatically or manually from the unstructured data for subsequent analysis. (Navamani, 2019; Song et al, 2020; Torres‐García et al, 2022; Vani Kumari & Usha Rani, 2020; Zlotogorski‐Hurvitz et al, 2019). Depending on the choice of feature extraction to be performed, the availability of outcome labels, and the need for exploratory analysis, the selection of optimal biomarkers and operationalizing the biomarker platform may proceed as a supervised, semi‐supervised, self‐supervised, or unsupervised learning task.…”

Hybrid Techniques with Support Vector Machine for Improving Artifact Ultrasound Images

“…KNN, SVM and Boosted Trees classifier are used for classification of these images. S. Vani Kumari and K. Usha Rani [16], the aim of this work is to find the feature extraction method that is best for classifying the medical images. Local Binary Patterns (LBP), Gray-Level-Run-Length-Matrix (GLRM), Completed Local Binary Patterns (CLBP), GLCM and Local Tetra Patterns (LTrP) are the most prominent feature extraction methods for medical images and are considered in this study.…”

Feature Extraction of Ophthalmic Images Using Deep Learning and Machine Learning Algorithms

“…Each pixel is compared to the pixels of its immediate neighborhood to get their local representation. LBP evaluates points near a central point and decides whether they are more than or less than the center point (i.e., it generates a binary response) [1,2]. Any pixels with values less than the center pixel were recorded as 0 and all other pixels were encoded as 1 in binary encoding.…”

“…In this paper, we did a survey on which feature extraction technique worked the best when it comes to a specific algorithm and for this model. We used extraction techniques such as LBP [1], SIFT, HOG [2] & Gabor and different algorithms such as SVM [1,3], CNN [4,5], KNN [2,6] & RFC [7] on this model. We were able to tell that Gabor as a feature extraction technique and RFC as an algorithm worked the best and gave good results for this specific model.…”