# Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning
## 2 Materials and Methods
### Cervical Cancer Classification  
As previously noted, advancements in diagnostic techniques are essential for improving patient outcomes. Recent studies have highlighted the potential for machine learning integration with traditional diagnostic techniques as a direction to enhance accuracy and efficiency of cervical cancer diagnosis. This section explores various cervical cancer classifi  
Figure 2: Examples of squamous intraepithelial lesions as classified by the Bethesda system: (**a**) normal (NSIL), (**b**) low (LSIL), (**c**) high (HSIL). cation approaches. Over the years, a variety of studies have explored the classification of cervical cancer using various techniques. In 2011, [PERSON] et al. described a texture description based on a Gabor filter to analyze nuclei structure and determine the feature vector [11]. For cancer grade classification, the authors use a clustering algorithm applied to the feature vector and calculate a ration between normal and abnormal cells. Based on that ratio, they assign a grade based on the grading scheme used by pathomorphologists with an accuracy of 89%. [PERSON] and [PERSON] described a classification system for Pap smear evaluation based on the gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP) [12]. The extracted features were then classified with support vector machines (SVM) and single-layer neural networks to reveal better performance of the SVM classifier. [PERSON] and [PERSON] [13] explored a comparable method to evaluate the performance of k-NN and SVM classifiers. Their framework, utilizing GLCM features, successfully classified Pap smear data with an accuracy of 90% for SVM and 88.3% for k-NN. In 2019, [PERSON] et al. described a fuzzy clustering method for cancer classification that achieved an accuracy of over 96%. The most recent papers report on the combination of serum infrared spectroscopy with machine learning and the application of deep learning techniques [14; 15]. Various other studies have also explored the use of deep learning in the classification of cervical cancer. [PERSON] and [PERSON], as well as [PERSON] and [PERSON], achieved high accuracies with deep learning models [16; 17]. [PERSON] and [PERSON] focused on the classification of MRI images and [PERSON] and [PERSON] on cervical images. The approach of [PERSON] and [PERSON] combines edge detection, wavelet transform, and CNN architecture for classification [18]. In 2023, [PERSON] et al. used transfer learning to improve classification performance. The above discussion has highlighted the integration of machine learning with traditional diagnostic techniques, significantly enhancing the accuracy and efficiency of cervical cancer diagnosis. However, most studies have focused on the classical Pap smear method rather than liquid-based cytology (LBC). Considering the differences between these methods, it is essential to develop a new specialized framework for LBC to enhance both the accuracy and efficiency of cervical cancer detection [19; 20]. [PERSON] et al. described their comparative study showing the supremacy of the liquid-based cytology method over the conventional method [20]. The authors presented a comprehensive statistical study demonstrating the superiority of LBC in decision-making processes. Such studies provided new challenges, sparking a new research area. The recent literature review shows how these challenges are being addressed. In 2019, [PERSON] et al. provided a description of a deep learning approach that was able to extract features and classify the LBC slides with accuracy of 95% in the best case [21]. [PERSON] et al. and [PERSON] et al. both demonstrated the potential of machine learning in this context, achieving good classification performance [22; 23]. [PERSON] et al. used a VGG16 convolutional network to classify between low- and high-risk cases achieving an accuracy of 70.8%, while the accuracy of [PERSON] et al. reached 90.7%. In 2023, [PERSON] et al. described the application of ResNet architecture for the classification of cervical cancer [24].