Novel data mining based image classification with Bayes, Tree, Rule, Lazy and Function Classifiers using fractional row mean of Cosine, Sine and Walsh column transformed images

Doctors and pathologists have long been concerned about determining the malignancy from cell images. This task is laborious, time-consuming and needs expertise. Due to this reason, automated systems assist pathologists in providing a second opinion to arrive at accurate decision based on cytology images. The classification of cytology images has always been a difficult challenge among the various image analysis approaches due to its extreme intricacy. The thrust for early diagnosis of cervical cancer has always fuelled the research in medical image analysis for cancer detection. In this paper, an investigative study for the classification of cytology images is proposed. The proposed study uses the discrete coefficient transform (DCT) coefficient and Haar transform coefficients as features. These features are given as a input to seven different machine learning algorithms for normal and abnormal pap smear images classification. In order to optimize the feature size, fractional coefficients are used to form the five different sizes of feature vectors. In the proposed work, DCT transform has given the highest classification accuracy of 81.11%. Comparing the different machine learning algorithms the overall best performance is given by the random forest classifier.

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“…Row mean refers to the set of averages of the intensity values of the respective rows [28]. The row mean is depicted in (2).…”

Section: Row Meanmentioning

confidence: 99%

Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Kalbhor¹,

Shinde²,

Jude

2022

TELKOMNIKA

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“…It is a classifier based on the bayesian probability hypothesis. The classifier makes an assumption that each and every feature is mutually exclusive of each other as well as makes an independent contribution to the outcome [14]. The algorithm utilizes (1),…”

Section: Naive Bayesmentioning

confidence: 99%

A robust pest identification system using morphological analysis in neural networks

Mishra

Singh²,

Brahmachari³

et al. 2019

PEN

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Timely detection of pests play a major role in agriculture. There exist many pest identification systems, but almost all of them suffer from the misclassification due to lighting, background clutter, heterogeneous capturing devices as well as the pest being partially visible or in the different orientation. This misclassification may cause tremendous yield loss. To mitigate this situation, we proposed an architecture to provide high classification accuracy under the aforementioned conditions using morphology and skeletonization along with neural networks as classifiers. We have considered the crop rice as a use case as it is the staple food grain of almost the entire population of India. The amount of pesticides used is highest in rice as compared to all other food grains. This paper offers a robust technique to identify the pests in rice crops. The performance of the proposed architecture is tested with an image dataset, and the experimental results reveal that our proposed approach provides better classification accuracy than the existing pest detection approaches in the literature. Furthermore, the experimental results also provide the performance comparison among the popular classifiers.

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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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Novel data mining based image classification with Bayes, Tree, Rule, Lazy and Function Classifiers using fractional row mean of Cosine, Sine and Walsh column transformed images

Cited by 17 publications

References 8 publications

Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

A robust pest identification system using morphological analysis in neural networks

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

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