Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3
This paper proposes a hybrid deep learning model integrating DenseNet201 and InceptionV3 to address the challenges in achieving accurate and reliable cervical cancer classification. Current models often exhibit limitations in balancing precision and recall, which are critical for dependable clinical...
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10835083/ |
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| author | Abhiram Sharma R. Parvathi |
| author_facet | Abhiram Sharma R. Parvathi |
| author_sort | Abhiram Sharma |
| collection | DOAJ |
| description | This paper proposes a hybrid deep learning model integrating DenseNet201 and InceptionV3 to address the challenges in achieving accurate and reliable cervical cancer classification. Current models often exhibit limitations in balancing precision and recall, which are critical for dependable clinical applications. The hybrid model leverages DenseNet201’s efficient feature reuse and InceptionV3’s capacity for handling multi-scale and hierarchical features through fine-tuning and feature fusion techniques. The methodology involves rigorous data preprocessing, including normalization, augmentation, and dataset splitting, to ensure robust training and validation. Feature extraction and dimensionality optimization are employed to identify the most critical and discriminative features for classification. The experimental setup utilizes Python, TensorFlow, and Keras within a GPU-enabled environment to handle computational demands effectively. Comprehensive evaluation metrics, including accuracy, precision, recall, and F1-score, indicate that the proposed model achieves an accuracy of 96.54%, 95.91% Presicion, 96.44% Recall and 96.17% F1 Score surpassing state-of-the-art models such as ResNet-50, DenseNet-201, InceptionV3, and Xception. Visualization tools, including high-resolution confusion matrices and ROC curves, further demonstrate the hybrid model’s capability to differentiate between cervical cancer cell classes accurately. Comparative analyses validate the model’s superior performance and its potential as a dependable tool for clinical implementation. This study presents a robust and efficient classification system that addresses the limitations of existing models. Future research will focus on further improving the system’s performance and investigating its applicability to other medical imaging tasks. The proposed model is expected to contribute significantly to early and accurate cervical cancer diagnosis, enhancing patient outcomes and supporting healthcare professionals in clinical decision-making. |
| format | Article |
| id | doaj-art-ac9f836ec6ea47fc9cc73204cf8d1543 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ac9f836ec6ea47fc9cc73204cf8d15432025-01-21T00:01:42ZengIEEEIEEE Access2169-35362025-01-01139868987810.1109/ACCESS.2025.352767710835083Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3Abhiram Sharma0https://orcid.org/0009-0004-0822-0603R. Parvathi1https://orcid.org/0000-0002-6633-9897School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaThis paper proposes a hybrid deep learning model integrating DenseNet201 and InceptionV3 to address the challenges in achieving accurate and reliable cervical cancer classification. Current models often exhibit limitations in balancing precision and recall, which are critical for dependable clinical applications. The hybrid model leverages DenseNet201’s efficient feature reuse and InceptionV3’s capacity for handling multi-scale and hierarchical features through fine-tuning and feature fusion techniques. The methodology involves rigorous data preprocessing, including normalization, augmentation, and dataset splitting, to ensure robust training and validation. Feature extraction and dimensionality optimization are employed to identify the most critical and discriminative features for classification. The experimental setup utilizes Python, TensorFlow, and Keras within a GPU-enabled environment to handle computational demands effectively. Comprehensive evaluation metrics, including accuracy, precision, recall, and F1-score, indicate that the proposed model achieves an accuracy of 96.54%, 95.91% Presicion, 96.44% Recall and 96.17% F1 Score surpassing state-of-the-art models such as ResNet-50, DenseNet-201, InceptionV3, and Xception. Visualization tools, including high-resolution confusion matrices and ROC curves, further demonstrate the hybrid model’s capability to differentiate between cervical cancer cell classes accurately. Comparative analyses validate the model’s superior performance and its potential as a dependable tool for clinical implementation. This study presents a robust and efficient classification system that addresses the limitations of existing models. Future research will focus on further improving the system’s performance and investigating its applicability to other medical imaging tasks. The proposed model is expected to contribute significantly to early and accurate cervical cancer diagnosis, enhancing patient outcomes and supporting healthcare professionals in clinical decision-making.https://ieeexplore.ieee.org/document/10835083/Cervical cancer classificationdeep learningDenseNet201feature fusionfine tuninghybrid model |
| spellingShingle | Abhiram Sharma R. Parvathi Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3 IEEE Access Cervical cancer classification deep learning DenseNet201 feature fusion fine tuning hybrid model |
| title | Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3 |
| title_full | Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3 |
| title_fullStr | Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3 |
| title_full_unstemmed | Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3 |
| title_short | Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3 |
| title_sort | enhancing cervical cancer classification through a hybrid deep learning approach integrating densenet201 and inceptionv3 |
| topic | Cervical cancer classification deep learning DenseNet201 feature fusion fine tuning hybrid model |
| url | https://ieeexplore.ieee.org/document/10835083/ |
| work_keys_str_mv | AT abhiramsharma enhancingcervicalcancerclassificationthroughahybriddeeplearningapproachintegratingdensenet201andinceptionv3 AT rparvathi enhancingcervicalcancerclassificationthroughahybriddeeplearningapproachintegratingdensenet201andinceptionv3 |