Feature selection, optimization and performance analysis of classifiers for biological images

Siji, K.K.; Mathew, Binitha Sara; Chandran, Rakhi; Shajeemohan, B. S.; S, Shanthini K

doi:10.1109/nccsn.2014.7001150

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…The best results from our experiments are compared with the state-of-the-art works: Shamir et al [22,58], Siji et al [59], and Meng et al [60] classified the same datasets from IICBU-2008 benchmark.…”

Section: Anova Analysis N-way Anovamentioning

confidence: 97%

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

González

Nava

Escalante-Ramı́rez

2018

Mathematical Problems in Engineering

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In recent years, discrete orthogonal moments have attracted the attention of the scientific community because they are a suitable tool for feature extraction. However, the numerical instability that arises because of the computation of high-order moments is the main drawback that limits their wider application. In this article, we propose an image classification method that avoids numerical errors based on discrete Shmaliy moments, which are a new family of moments derived from Shmaliy polynomials. Shmaliy polynomials have two important characteristics: one-parameter definition that implies a simpler definition than popular polynomial bases such as Krawtchouk, Hahn, and Racah; a linear weight function that eases the computation of the polynomial coefficients. We use IICBU-2008 database to validate our proposal and include Tchebichef and Krawtchouk moments for comparison purposes. The experiments are carried out through 5-fold cross-validation, and the results are computed using random forest, support vector machines, naïve Bayes, and k-nearest neighbors classifiers.

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Section: Anova Analysis N-way Anovamentioning

confidence: 97%

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

González

Nava

Escalante-Ramı́rez

2018

Mathematical Problems in Engineering

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“…Siji et al enhanced the performance of a content-based biological image retrieval system by selecting discriminative feature sets from the set of canonical features [26]. Canonical features are extracted using Wndchrm [23] and are assigned to 4 separate feature sets.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features

Czaplewski¹,

Dzwonkowski²,

Panas³

2022

JTIT

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Nematodes Caenorhabditis elegans (C. elegans) have been used as model organisms in a wide variety of biological studies, especially those intended to obtain a better understanding of aging and age-associated diseases. This paper focuses on automating the analysis of C. elegans imagery to classify the muscle age of nematodes based on the known and well established IICBU dataset. Unlike many modern classification methods, the proposed approach relies on deep learning techniques, specifically on convolutional neural networks (CNNs), to solve the problem and achieve high classification accuracy by focusing on non-handcrafted self-learned features. Various networks known from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have been investigated and adapted for the purposes of the C. elegans muscle aging dataset by applying transfer learning and data augmentation techniques. The proposed approach of unfreezing different numbers of convolutional layers at the feature extraction stage and introducing different structures of newly trained fully connected layers at the classification stage, enable to better fine-tune the selected networks. The adjusted CNNs, as featured in this paper, have been compared with other state-of-art methods. In anti-aging drug research, the proposed CNNs would serve as a very fast and effective age determination method, thus leading to reductions in time and costs of laboratory research.

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A Systematic Comparison and Evaluation of Supervised Machine Learning Classifiers Using Headache Dataset

Aljaaf

Al‐Jumeily

Hussain

et al. 2015

Lecture Notes in Computer Science

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Feature selection, optimization and performance analysis of classifiers for biological images

Cited by 4 publications

References 10 publications

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features

A Systematic Comparison and Evaluation of Supervised Machine Learning Classifiers Using Headache Dataset

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