Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

Anupama, C. S. S.; Jose, T. J. Benedict; Eid, Heba F.; Aljehane, Nojood O.; Al‐Wesabi, Fahd N.; Obayya, Marwa; Hilal, Anwer Mustafa

doi:10.32604/cmc.2022.022701

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…The deep learning models have proven to have high accuracy but the implementation of the deep learning architectures necessitates a large amount of data and memory computation [25]. So along with these basic contents the transform domain is explored for image compression and image classification.…”

Section: Table 1 Summary Of Recent Work Done On Pap Smear Cytology Im...mentioning

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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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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Section: Table 1 Summary Of Recent Work Done On Pap Smear Cytology Im...mentioning

confidence: 99%

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

Kalbhor¹,

Shinde²,

Jude

2022

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification

Vaiyapuri

Alaskar²,

Syed³

et al. 2022

Computers and Electrical Engineering

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Bio-Inspired Algorithms for Wireless Network Optimization

Eid

2023

Advances in Wireless Technologies and Telecommunication

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Significant research interest has been shown in wireless sensor networks (WSNs), particularly in the context of internet of things (IoT) technologies. However, obtaining the optimal WSN lifespan network is a challenging issue that needs to be analyzed critically prior to any network setup. In recent years, new bio-inspired algorithms have been developed drawing their inspiration from biological and natural phenomena. Bio-inspired optimization algorithms have been compared with the traditional optimization algorithms and are showing promise as a solution to complex real-life problems. This chapter provides a survey and tutorial of recent research trends and development efforts addressing WSN issues by using bio-inspired optimization algorithms. The key intention of this work is to serve as a foundation for analysis of the emerging area of bio-inspired algorithms and multi-objective bio-inspired optimization algorithms for solving the underlying issues in wireless sensor networks.

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Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

Cited by 3 publications

References 20 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

Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification

Bio-Inspired Algorithms for Wireless Network Optimization

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