Machine learning for assisting cervical cancer diagnosis: An ensemble approach Abstract   Cervical cancer remains one of the most prevalent gynecologic malignancies, worldwide. As cervical cancer is a highly preventable disease, therefore, early screening represents the most effective strategy to minimize the global burden of cervical cancer. However, due to scarce awareness, lack of access to medical centers, and highly expensive procedures in developing countries, the vulnerable patient populations cannot afford to undergo examination regularly. A novel ensemble approach is presented in this paper to predict the risk of cervical cancer. By adopting a voting strategy, this method addresses the challenges associated with previous studies on cervical cancer. A data correction mechanism is proposed to improve the performance of the prediction. A geneassistance module is also included as an optional strategy to enhance the robustness of the prediction. Multiple measurements are performed to evaluate the proposed method. The results indicate that the likelihood of developing cervical cancer can be effectively predicted using the voting strategy. Compared with other methods, the proposed method is more scalable and practical.

Cervical Cancer Diagnosis using CervixNet  A Deep Learning Approach Cervical cancer affects 570,000 women globally and is among the most common causes of cancerrelated deaths. Cervical cancer is caused due to the Human Papilloma Virus (HPV) which leads to abnormal growth of cells in the cervix region. Regular testing for HPV in women has helped reduce the death rate in developed countries. However, developing nations are still struggling to provide lowcost solutions due to the lack of affordable medical facilities. The skewed ratio of the oncologists to patients has also aggravated the problem. Motivated by the Deep Learning solutions in Biomedical imaging, we propose a novel CervixNet methodology which performs image enhancement on cervigrams followed by Segmenting the Region of Interest (RoI) and then classifying the RoI to determine the appropriate treatment. For the classification task, a novel Hierarchical Convolutional Mixture of Experts (HCME) algorithm is proposed. HCME is capable of tackling the problem of overfitting, given that small datasets are an inherent problem in the field of biomedical imaging. Our proposed methodology has outperformed all the existing methodologies on publicly available Intel and MobileODT Kaggle dataset giving an Accuracy of 96.77% and kappa score of 0.951. Hence, the results obtained validate our approach to provide first level screening at a low cost.