# Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap smear Images
## 1 Introduction
The studies of computer-assisted screening of cervical cytology can be classified into the cell level classification, smear level classification, applied segmentation algorithms, usage of features, and classifiers. In cell level classification, each input image has only one cell, which is classified into the normal or abnormal cell [12]. In smear level classification, the input image includes one more cell and also other artifacts [13]. There are many different algorithms that have been used for image segmentation, such as clustering [14], thresholding [15; 16], edge detection [17], and watershed transformation [18]. The watershed segmentation was firstly proposed by [PERSON] and [PERSON] [19]. Over segmentation is one of the significant problems in watershed algorithms. Our proposed study developed an improved marker-based watershed algorithm that provided better results than the traditional algorithm and helped to reduce the over-segmentation problem. Most research studies have used a watershed algorithm to split overlapping cells' nuclei [20]. But in our multi-cells Pap smear images, overlapping cytoplasm was mostly contained, as shown in Figure 1 and a few overlapping nuclei were found. Therefore, the watershed transform algorithm was used for overlapping cytoplasm segmentation. Recently, [PERSON] et al. [21] had proposed the segmentation of nuclei and cytoplasm regions using the gradient vector flow method (GVF). However, this study only used a single cell as an input to be analyzed. While each cell slide can contain over 10,000 cells [22], the approach using a single cell cannot be enough in real cases. However, it does not solve the issue of the overlapping cells. Many different algorithms have been proposed to solve the problem of overlapping cells [23]. Among them, marker controlled watershed transformation is one of the most used methods. The main problem in this approach is over-segmentation problems [24]. In feature extraction, mostly used features are shape [25], texture [26], and color features. The major advantage of using texture attribute is its simplicity. Therefore, the texture feature was extracted using GLCM (Gray Level Co-Occurrence Matrix) in our feature extraction stage. The mostly used classifiers in the multi-cell cervical image analysis are support vector machine (SVM) [27], LDA (Linear Discriminant Analysis) [28], k-nearest neighbor (KNN) [29], and ANN (Artificial Neural Networks) [30]. There have been many research studies about cervical cancer detection, but most studies have only targeted the segmentation of nuclei regions [31]. The segmentation of cytoplasm regions is also essential. The features that are extracted from cytoplasm regions are helpful for the classification of abnormal cells [32]. This paper is divided into four parts. Part 1 introduces about cervical cancer screening system and discusses the previous research studies. Part 2 expresses the explanations of the datasets used and the methodology that is used in this study. Part 3 indicates the results of the proposed system. Finally, Part 4 presents the conclusion.