# Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning
## 1 Introduction  
Looking at the latest statistics on cervical cancer, one can notice that the number of incidents of this disease decreases. However, it remains a significant challenge both in oncology and in social contexts. According to the World Health Organization (WHO), more than 570,000 new cases of cervical cancer are reported each year, and approximately 311,000 of these cases result in death [1]. In Poland, the five-year survival rate in years 2010-2015 was 48.3% and in Europe it was 62.1% [2]. For cancer, it is a well-known fact that a faster diagnosis will increase the chances of successful treatment for patients [3]. For cervical cancer, risk factors are well known and can be taken into account for a better diagnosis process. These factors can be divided into two categories [4]:* **Main factors** that include age of the patient, chronic inflammation of the HPV virus, early sexual relationship experience, large number of sex partners, large number of child deliveries, long smoking habits, and low economic status. * **Possible factors** that include long use of contraceptive pills, a diet rich in antioxidants, HIV infection, as well as frequent, untreated inflammations. The main purpose of diagnosis is to quickly and accurately detect the disease and determine its histological type. The process starts with a patient interview and a physical examination. If a suspicious region is found, a cytological examination is performed. Current diagnostic workflows often rely on cytological examinations, such as Pap smears or liquid-based cytology (LBC), to detect cervical abnormalities. However, these methods are often subjective and depend heavily on the expertise of pathomorphologists. Despite offering improved slide quality and reduced sample variability, LBC still suffers from interpretive limitations, leading to inconsistencies in diagnosis. Most existing methods try to solve the problem with deep learning models trained on large data sets or focus on slide-level classification, which limits their applicability in resource-constrained environments. To address these gaps, this study proposes a novel approach that integrates handcrafted feature-based methods, derived from clinically relevant diagnostic criteria. Unlike existing methods that focus on slide-level classification or rely on resource-intensive deep learning models, this framework works at the per-cell level, aligning closely with the diagnostic process used by pathologists. By focusing on per-cell classification, this study aims to advance automated diagnosis in resource-constrained environments. In addition, traditional diagnostic systems, such as the Bethesda system, often focus on broad classifications, such as normal versus abnormal, which may lack the specificity required for detailed treatment planning. Recent developments in computer vision and machine learning, especially in the area of convolutional neural networks, have demonstrated high accuracies in medical image analysis and classification. However, most of these approaches focus on distinguishing between normal and abnormal samples, rarely providing the granular categorization necessary for effective treatment planning. These limitations make them less practical in resource-constrained environments or for granular classifications such as Normal Squamous Intraepithelial Lesion (NSIL), Low-Grade Squamous Intraepithelial Lesion (LSIL) and High-Grade Squamous Intraepithelial Lesion (HSIL). To address these challenges, this study proposes a framework that combines handcrafted feature-based methods with deep learning techniques, such as convolutional neural networks, to classify LBC images into specific diagnostic categories: NSIL, LSIL or HSIL. According to the literature, hybrid approaches that integrate handcrafted features with deep learning have shown significant promise in the advancement of diagnostic frameworks. For example, [PERSON] et al.