Automated System for Cervical Abnormality Detection using Machine Learning Cervix is a cylindershaped neck of tissue that connects uterus and vagina. It is a part of the female reproductive system. Medical diagnosis is a process of detecting a problem with the help of symptoms. In this work, we have developed an automated system for diagnosis of various cervical abnormalities like cervical cancer, ascus, metaplasia, erosion, polyp, inflammation, candidiasis, and nabothian cysts. The system performs diagnosis using images captured from pre and postVIA (Visual Inspection with acetic acid) and VILI (Visual Inspection with Lugolu0027s Iodine) tests. We apply transfer learning and machine learning algorithms for effective abnormality detection. In this work, we have developed automated diagnostic systems for binary (normal versus abnormal) and multi (3) class classification of abnormalities based on images taken by doctors. We will further evaluate all the approaches and report the accuracy scores for them.

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.