Identification of Malignancy from Cytological Images Based on Superpixel and Convolutional Neural Networks

Mitra, Shyamali; Dey, Soumyajyoti; Das, Nibaran; Chakrabarty, Sukanta; Nasipuri, Mita; Naskar, Mrinal Kanti

doi:10.1007/978-981-13-7334-3_8

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…With the advent of artificial intelligence and deep learning in the domain of medical sciences and healthcare [4], it is more becoming to lie on the results predicted by this decision-support system [5] to undermine the observerbias issues. In this paper, we seek to develop an alternative approach that utilizes the deep learning-based feature extraction and optimization algorithm that gives excellent multi-class classification accuracy, performing robustly and outperforming several existing methods like [6,8,12,15,28,29,37].…”

Section: Introductionmentioning

confidence: 99%

Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

Basak

Kundu

Chakraborty³

et al. 2021

SN COMPUT. SCI.

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Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes deep learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts deep feature from several convolution neural network (CNN) models and uses a two-step feature reduction approach to ensure reduction in computation cost and faster convergence. The features extracted from the CNN models form a large feature space whose dimensionality is reduced using principal component analysis while preserving 99% of the variance. A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the grey wolf optimizer, thus improving the classification performance. Finally, the selected feature subset is used to train an support vector machine classifier for generating the final predictions. The proposed framework is evaluated on three publicly available benchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev Pap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5-class) dataset achieving classification accuracies of 99.47, 98.32 and 97.87%, respectively, thus justifying the reliability of the approach. The relevant codes for the proposed approach can be found in: https:// github. com/ DVLP-CMATE RJU/ Two-Step-Featu re-Enhan cement.

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Section: Introductionmentioning

confidence: 99%

Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

Basak

Kundu

Chakraborty³

et al. 2021

SN COMPUT. SCI.

Self Cite

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“…With the advent of artificial intelligence and deep learning in the domain of medical sciences and healthcare [4,8,6], it is more becoming to lie on the results predicted by this decision-support system [7,12] to undermine the observer-bias issues. In this paper, we seek to develop an alternative approach that utilizes the Deep learning-based feature extraction and optimization algorithm that gives excellent multi-class classification accuracy, performing robustly and outperforming several existing methods like [18,40,11,9,31,32,15].…”

Section: Introductionmentioning

confidence: 99%

Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection

Basak,

Kundu,

Chakraborty

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes Deep Learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts Deep feature from several Convolution Neural Network models and uses a two-step feature reduction approach to to ensure reduction in computation cost and faster convergence. The features extracted from the CNN models form a large feature space whose dimensionality is reduced using Principal Component Analysis while preserving 99% of the variance. A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the Grey Wolf Optimizer, thus improving the classification performance. Finally, the selected feature subset is used to train an SVM classifier for generating the final predictions. The proposed framework is evaluated on three publicly available benchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev Pap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5class) dataset achieving classification accuracies of 99.47%, 98.32% and 97.87% respectively, thus justifying the reliability of the approach. The relevant codes for the proposed approach can be found in: GitHub

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Malignancy Identification from Cytology Images Using Deep Optimal Features

Dey,

Nasipuri,

Ghosh

et al. 2023

Algorithms for Intelligent Systems

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Identification of Malignancy from Cytological Images Based on Superpixel and Convolutional Neural Networks

Cited by 4 publications

References 24 publications

Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection

Malignancy Identification from Cytology Images Using Deep Optimal Features

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