Open Access
Research Article
Deep ensemble learning model for cervical cancer disease classification on image dataset
*Sonam JunejaCorresponding authorsonam.december@gmail.comDepartment of Computer Science and Engineering Chandigarh University Gharuan Mohali, Punjab, 140413, IndiaView full profile → , Shikha Atwalshikhaatwal780@gmail.comDepartment of Computer Science and Engineering Chandigarh University Gharuan Mohali, Punjab, 140413, IndiaView full profile → , Reema Goyalreemagoyal01@gmail.comDepartment of Computer Science and Engineering Chitkara UniversityRajpura, Punjab, 140401, IndiaView full profile → , Bhoopesh Singh Bhatibhoopesh.cse@gmail.comDepartment of Computer Science and Engineering Indian Institute of Information TechnologySonepat, Haryana, 131001, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 14 Aug 2024
- Published Online:
- 01 Jan 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1943
- Pages:
- 263–272
Abstract
Early detection is the hallmark of ensuring better patient outcomes and successful treatment for most forms of cancer. In our proposed model, we classify the cervical cancer disorders through the developed deep ensemble learning model that was trained on a large dataset of colposcopy photos. It is evident that combining deep learning with an ensemble methodology would make colposcopy data-driven cervical cancer screening more accurate and robust. The proposed model was experimented with various known performance measures such as sensitivity, specificity, accuracy, PPV, and NPV. The experimental outcome results find this ensemble deep learning model to perform outstandingly well with remarkable robustness and outstanding accuracy in ‘detecting’ cervical cancer over individual models. Integrating the Colposcopy Ensemble Network architecture developed to address the problem of cervical cancer detection, this model increases the overall sensitivity and accuracy. Thus, it is highly efficient given other performance standards and, what is even more important, sets the scene for investigating and fostering the development of ensemble learning models in classifying various diseases based on medical images
Keywords
Subject Classifications
68Txx68Uxx
References
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