Budanis Dwi Meilani, Siti Nurmuslimah, Lisetyo Ariyanti, Miswanto, Yuli Panca Asmara, Aeri Rachmad
Cervical cancer remains one of the leading causes of cancer-related mortality among women worldwide, particularly in low- and middle-income countries. Early and accurate detection is therefore essential for improving patient outcomes. However, traditional cytological examination methods are often time-consuming and subject to inter-observer variability. This study proposes a deep learning–based framework for cervical cell image classification using a multi-model convolutional neural network architecture. The proposed framework integrates three lightweight convolutional neural network (CNN) architectures—MobileNet V3, EfficientNet V2, and ShuffleNet V2—as parallel feature extractors to capture complementary visual representations. Feature maps generated by the individual models are fused to form a richer representation before the final classification stage. To evaluate the effectiveness of the proposed approach, four experimental scenarios were designed using different optimization algorithms, including Adam, Root Mean Square Propagation (RMSProp), Stochastic Gradient Descent (SGD), and Adamax, with a fixed learning rate of 0.01. The dataset was divided using an 80:20 training and testing strategy to ensure reliable performance evaluation. Experimental results demonstrate that the proposed multi-model architecture significantly improves classification performance. The best results were achieved using the SGD optimizer, reaching a validation accuracy of 94.44% and an F1-score of 94.50%. These findings indicate that combining multiple CNN architectures with appropriate optimization strategies can enhance feature representation and improve the accuracy of cervical cancer image classification. Copyright: © 2026 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
Department of Informatics, Institut Teknologi Adhi Tama Surabaya, Surabaya, 60117, Indonesia; English Literature Study Program, Faculty of Languages and Arts, Universitas Negeri Surabaya, Surabaya, 60213, Indonesia; Department of Mathematics, Faculty of Science and Technology, University of Airlangga, Surabaya, 60115, Indonesia; Faculty of Engineering and Quantity Surveying, INTI International University, Nilai, 71800, Malaysia; Department of Informatics, Faculty of Engineering, University of Trunojoyo Madura, Bangkalan, 69162, Indonesia
