Work

TransPapCanCervix : An Enhanced Transfer Learning‐Based Ensemble Model for Cervical Cancer Classification

Year: 2025

Type: article

Abstract: 30.0 Cervical cancer, like many other cancers, is most treatable when detected at an early stage. Using classification methods helps find early signs of cancer and small tumors. This allows doctors to act quickly and offer treatments that might cure the cancer. This study presents a comprehensive approach to the classification of squamous cell carcinoma (SCC) leveraging a dataset comprising 1140 single-cell images sourced from Herlev. A variety of deep learning models, including DenseNet121, DenseNet169, InceptionResNet, XceptionNet, ResNet50, and ResNet101, are employed both individually and in ensembles, demonstrating their efficacy in classifying diverse cellular features. To validate the robustness of results, k-fold cross-validation is conducted, further affirming the effectiveness of the proposed methodology. Thorough exploration produces a precise and effective model for SCC classification, providing detailed insights into both normal and abnormal cell types. These findings show that transfer learning-based deep neural networks and ensemble methods can improve the diagnostic capabilities by 98% accuracy, of SCC classification systems for different cell types.

Source: Computational Intelligence

DOI: 10.1111/coin.70027

Authors: Barkha Bhavsar, Bela Shrimali

Institutions: Kadi Sarva Vishwavidyalaya, Nirma University

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,860