# Earth Mover's Distance-Based Tool for Rapid Screening of Cervical Cancer Using Cervigrams
## 4 Discussion
In the case of cervical cancer, screening is performed through liquid-based cytology and colposcopy. In liquid-based cytology, a major hindrance is the acquisition of a sample and the lack of specialized professionals to make the diagnosis. However, in the recent past, there have been tremendous technological advancements with the advancements of AI (artificial intelligence) [25]. In a resource-limited country such as India, there is an urgent and unmet need to clearly identify reliable and robust visual cues in cervigrams. Furthermore, these cues should be robust enough to remain invariantly un-occluded under different optical transformations and even in the presence of unwanted objects in images such as hair and other body parts. Such a process will eliminate artifacts and other irrelevant features to seamlessly segment structures for grading measurements of cervical cancer lesions. The automation of this feature ensures that the algorithm detects structural aberrations in cervigrams in a precise and timely manner to make cervical cancer detection even more reliable [26]. The optical clarity of an image is paramount for visual inspection by a camera with direct human eye inspection. Optical aberration, color misrepresentation, and specular reflection in cervigrams are other challenges associated with conventional colposcopes [27]. Our approach takes care of these problems by facilitating the provisioning of much sharper and focused images necessary for the delineation and identification of cancerous lesions. Using image processing-based automated detection of cervical cancer can increase precision and minimize errors. When detected and managed at an early stage, the clinical prognosis for cervical cancer is favorable [28]. This indeed necessitates the need for a technology-driven approach for developing an effective and precision-oriented triage system for facilitating not only large-scale screening but also prioritizing patients for therapeutic intervention. This can minimize the recovery period through screening and follow-up. Taking into account the conditions, parameters, limitations, and the knowledge sharing from manual to computational colposcopy, we tried to incorporate ubiquitous information related to the labeling/tagging of the cervix and have developed an artificial intelligence-enabled intelligent decision support system capable of serving as an adjunct clinical aide for facilitating rapid screening for the detection of cancerous lesions. In this study, we introduced an algorithm-based colposcopic image (cervigrams) analysis and diagnostic technology that uses the attribution method to identify the cervix types of Indian subjects [29; 30]. The R software (version 2021.09.0 Build 351) libraries included advanced tools for medical image analysis, which deal with the quantification of the colposcope-derived cervigrams, thereby reducing the burden and time of medical professionals by representing images as numerical data for analysis apart from conventional image processing procedures [31; 32]. Limitations:  
* The computational part of the proposed algorithm heavily relies on cervigrams acquired by a colposcope; hence, its ability to correctly tag cervical conditions is limited by the truthfulness and resolution of the cervigrams. * This tool was built on the basis of training data, which were used for concept development and its validated implementation. Large numbers of heterogeneous cervigrams from all across the country are required for refining the tool and then should be deployed in the community with proper settings for other lifestyle and genetic parameters.