# Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning
## 3 Cervical Cancer Database  
To evaluate a proposed framework, we assembled a database of cytological images of cervix uteri smears prepared using the liquid-based cytology (LBC) method according to the following procedure:  
* **Specimen Collection**--the samples were collected with a brush and suspended in a preservation solution.  
* **Specimen processing--**the solution was then processed through an automated separator to isolate cells from debris and placed on glass slides.
* **Slide staining--**slides were stained using the Papanicolaou technique, which employs specific staining agents to highlight cellular details for morphological analysis. In general, staining methods like hematoxylin and eosin or Papanicolaou staining enhance the visibility of cells and nucleus structures.
* **Slide digitization--**the stained slides were acquired from the Department of Patomorphology of the Pomeranian Medical University in Szczecin, Poland, and digitized in the Department of Pathology and Oncological Cytology at the Medical University of Wroclaw, Poland, using an Olympus BX 51 microscope equipped with an Olympus DP72 camera attached to the microscope head. The images were captured using the Olympus CellD software at a resolution of 300 dpi, with dimensions of \(2510\times 1540\) pixels. The collected database contains 263 LBC cytology images, with 158 classified as Normal Squamous Intraepithelial Lesion, 70 as High Squamous Intraepithelial Lesion, and 35 as Low Squamous Intraepithelial Lesion. For cell shape analysis, we utilized ImageJ software to extract single cell images, resulting in 428 images: 233 NSIL, 110 HSIL and 85 LSIL, all annotated at the per-cell level. For data curation, the following inclusion and exclusion criteria were applied:
* **Inclusion criteria:*
* Slides of adequate quality and clear diagnostic classification by expert pathologists
* **Exclusion criteria:*
* Low slides quality, ambiguous diagnoses, or insufficient cellular material for accurate segmentation.  
The images were retrospectively collected at a single institution to ensure consistency in sample preparation and diagnostic annotations. Although this limits the diversity of the data set, it provides a high-quality foundation for per-cell classification, which is not available in most public data sets.
* **Case validation--**in the next step, an expert pathomorphologist examined the slides to categorize the samples into NSIL, LSIL, or HSIL based on the Bethesda system criteria. This step provided ground-truth labels for training and validating the classification algorithms.  
The methods described in this section establish a framework for automated classification of cervical cancer cytology data. By integrating liquid-based cytology imaging, advanced feature extraction, dimensionality reduction techniques, and machine learning, this study aims to improve the accuracy and precision of cervical cancer diagnosis. The next section provides a description of the results obtained from the implementation of the proposed methodology. It includes an evaluation of the effectiveness of feature selection methods in reducing dimensionality, classification performance across different machine learning approaches, and comparisons between handcrafted feature-based methods and a convolutional neural network. Performance metrics such as _accuracy, precision, recall_, and computational efficiency are analyzed to validate the proposed approach.