Title: A hybrid deep learning approach for cervical cancer segmentation and classification

Authors: Divya Francis; Bharath Subramani

Addresses: Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu – 624622, India ' Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu – 624622, India

Abstract: One of the most common cancers worldwide is cervical cancer and is ranked as fourth among all gynaecological malignancies. In this research, a model named fractional gorilla chimp optimisation-based Shepard convolutional neural network (FGChO-based ShCNN) is developed for the classification of cervical cancer. Here, the image denoising is done by the adaptive bilateral filter. Furthermore, the U-Net++ model with gorilla chimp optimisation (GChO) is used for segmentation purposes. After that, the necessary features like grey level co-occurrence matrix (GLCM) features and texture features are extracted. Then, the automatic cervical cancer classification is executed with FGChO-based ShCNN wherein the ShCNN undergoes training using FGChO. The proposed FGChO-based ShCNN has the accuracy of 93.1%, false positive rate (FPR) of 0.055, positive predictive value (PPV) of 90.5%, negative predictive value (NPV) of 89.9%, true negative rate (TNR) of 94.5%, and true positive rate (TPR) of 92.4%.

Keywords: cervical cancer; Unet++; Shepherd convolution neural network; adaptive bilateral filter; moment invariant feature; gorilla chimp optimisation; GChO; grey level co-occurrence matrix; GLCM.

DOI: 10.1504/IJAHUC.2024.142700

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.47 No.4, pp.209 - 226

Received: 27 Dec 2023
Accepted: 12 Jun 2024
Published online: 18 Nov 2024 *

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