Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning

Abstract

The main purpose of cervical cancer diagnosis is a correct and rapid detection of the disease and the determination of its histological type. This study investigates the effectiveness of combining handcrafted feature-based methods with convolutional neural networks for the determination of cancer histological type, emphasizing the role of feature selection in enhancing classification accuracy. Here, a data set of liquid-based cytology images was analyzed and a set of handcrafted morphological features was introduced. Furthermore, features were optimized through advanced selection techniques, including stepwise and significant feature selection, to reduce feature dimensionality while retaining critical diagnostic information. These reduced feature sets were evaluated using several classifiers including support vector machines and compared with CNN-based approach, highlighting differences in accuracy and precision. The results demonstrate that optimized feature sets, paired with SVM classifiers, achieve classification performance comparable to those of CNNs while significantly reducing computational complexity. This finding underscores the potential of feature reduction techniques in creating efficient diagnostic frameworks. The study concludes that while convolutional neural networks offer robust classification capabilities, optimized handcrafted features remain a viable and cost-effective alternative, particularly when the data count is limited. This work contributes to advancing automated diagnostic systems by balancing accuracy, efficiency, and interpretability.