# Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning
## 5 Discussion and Conclusions  
In this paper, we present a classification framework for liquid-based cytology (LBC) slides for automated cervical cancer diagnosis. The methodology described here uses the conventional classification approach in which morphological features are calculated. To our knowledge, this is the first automated classification approach for slides prepared using the LBC mmethod rather than the typical Pap smear method that uses hand-crafted features. Using feature reduction methods, the framework reduces computational demands without sacrificing diagnostic accuracy. This approach addresses critical challenges in resource-constrained regions, where access to high-end computational resources and large data sets for deep learning is limited. Furthermore, this study shows the significant clinical implications of integrating machine learning with traditional cytological techniques in the diagnosis of cervical cancer. The application of machine learning, especially a Support Vector Machine (SVM) classifier, improves diagnostic accuracy and consistency. This approach reduces the risk of misdiagnosis and ensures standardized diagnostic processes by simplifying the examination of cytological slides. The results clearly demonstrate that the presented framework is capable of making accurate predictions. As observed, the SVM classifier achieved the best performance, with an error rate of only 7%, while also providing the highest _precision_ rate. The _recall_ values are at an acceptable level, allowing us to conclude that among the five tested classifiers, SVMs are the optimal choice. Additionally, our findings highlight the computational efficiency of the proposed framework. A comparison between conventional methods and CNN-based feature extraction revealed that conventional methods are approximately 1.38 times faster for processing a single cell image and require slightly less memory. This efficiency makes conventional methods a better choice for resource-constrained clinical environments, further emphasizing the practicality of handcrafted features combined with SVM classifiers. Moreover, our study shows that the stepwise and significant feature selection methods offer the best trade-off between feature vector size and classification accuracy. As mentioned in Section 2.5, feature selection can enhance the efficiency and interpretability of the diagnostic framework. Recent research has introduced sophisticated algorithms such as Opposition-based Harmony Search to select features, which improved performance on Pap smear image classification tasks [49]. Similarly, the integration of deep learning techniques, such as transfer learning with DenseNet and Vision Transformers, allowed the achievement of up to 98.6% classification accuracy [25; 26]. Although these studies describe very good results, they also face challenges, including computational demands and reliance  
Figure 7: Classification _precision_ vs. feature-selection method. on large data sets. These constraints can limit their applicability in resource-constrained environments or for granular classifications like HSIL and LSIL. In contrast, our approach focuses on handcrafted morphological features combined with advanced feature reduction techniques, which significantly minimize computational requirements without sacrificing diagnostic accuracy. This makes our method particularly advantageous in clinical settings where resources and data are limited. One limitation of the proposed methodology is the relatively small size of the data set (263 images), as it may affect the generalizability of the results. Furthermore, the data set was collected from a single institution, which may introduce biases due to the lack of diversity in patient demographics or preparation methods. However, the data set was specifically curated for per-cell classification, ensuring high-quality annotations aligned with diagnostic workflows. To address these limitations, future research should focus on validating the framework on a larger and more diverse data set.