Classifying Pap Smear Images with an Advanced Composite Random Forest Model

Classifying Pap Smear Images with an Advanced Composite Random Forest Model
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : Sharmistha Bhattacharjee, Dipankar Ray, Diganta Saha, D. Sobya
DOI : 10.14445/22315381/IJETT-V70I10P230

How to Cite?

Sharmistha Bhattacharjee, Dipankar Ray, Diganta Saha, D. Sobya, "Classifying Pap Smear Images with an Advanced Composite Random Forest Model," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 307-318, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P230

Abstract
Manual screening and diagnosis of conventional Pap-smear slides for cervical cancer diagnosis is slow and suffers from human error. Here we have proposed a hybrid-deep-learning model achieved using k-means cluster and Random Forest models, which aims to identify the most prevailing characteristics of cervical tissues and classify them into different cytopathological classes. Just because the texture, shape (morphometric), and color of the nucleus and cytoplasm together or individually play a vital role in PAP smear image classification, fifteen prominent features are extracted based on it to classify images collected from the Herlev Pap Smear dataset. Gray Level Covariance Matrix and Gabor Filter helped extract the texture-based features, whereas morphometric and color-based characteristics were abstracted using Canny's edge detection and histogram analysis. In addition, a new and advanced cutting-edge compound random forest model is constructed to categorize the PAP smear photos. It was noted that the suggested hybrid approach offers up to 99% effectiveness. Additionally, this study also demonstrated a thorough comparison of the suggested model. It was observed that the suggested model also performs admirably when measured against Support Vector Machine (SVM) and Deep-Multilayer Perceptron methods.

Keywords
Cervical Cancer, Herlev Pap Smear dataset, Gray Level Covariance Matrix, Random Forest, Deep-Multilayer Perceptron.

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