Cervical cancer remains a significant cause of female mortality worldwide, primarily due to abnormal cell growth in the cervix. This study proposes an automated classification method to enhance detection accuracy and efficiency, addressing contrast and noise issues in traditional diagnostic approaches. The impact of image enhancement on classification performance is evaluated by comparing transfer learning-based Convolutional Neural Network (CNN) models trained on both original and enhanced images. This study employs transfer learning with pre-trained CNNs to classify preprocessed Pap smear images into three categories. Data augmentation, including rotations, flips, and shifts, enhances variability and prevents overfitting. The OneCycle learning rate schedule dynamically adjusts the learning rate, improving training efficiency. To enhance image quality, the Denoised Pairing Adaptive Gamma with Clipping Histogram Equalization (DPAGCHE) method improves contrast and reduces noise. The evaluation involves five pre-trained CNN models and the publicly available Herlev dataset, implemented in MATLAB Online. The ResNet50 model trained on the DPAGCHE-enhanced dataset achieves the highest classification performance, with 84.15% accuracy, along with improved specificity, recall, precision, and F1-score. ResNet50’s residual connections mitigate vanishing gradient issues and enhance deep feature extraction. Accordingly, the DPAGCHE preprocessing significantly improves classification performance, leading to a 53.65% increase in F1-score and 44.29% in precision. In contrast, the Baseline CNN reaches only 66.67% accuracy, highlighting the advantage of deeper architectures combined with enhanced preprocessing. These findings suggest integrating DPAGCHE-enhanced preprocessing with deep learning improves automated cervical cancer detection. In particular, ResNet50 demonstrates the best performance, reinforcing the effectiveness of contrast enhancement and noise reduction in aiding classification models.

Cervical cancer remains a major global health issue and is the second most common cancer affecting women worldwide. Early detection is crucial for effective treatment but remains challenging due to the asymptomatic nature of the disease and the visual complexity of cervical cell structures, which are often affected by inconsistent staining, poor contrast, and overlapping cells. This study aims to classify cervical cell images using Artificial Intelligence (AI) techniques by comparing the performance of Convolutional Neural Networks (CNN), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The Herlev Pap smear image dataset was used for experimentation. In the preprocessing phase, images were resized to 100 × 100 pixels and enhanced through grayscale conversion, Gaussian smoothing for noise reduction, contrast stretching, and intensity normalization. Segmentation was performed using region-growing and active contour methods to accurately isolate cell nuclei. All classifiers were implemented using MATLAB. Experimental results show that CNN achieved the highest performance, with an accuracy of 85%, precision of 86.7%, and sensitivity of 83%, outperforming both SVM and KNN. These findings indicate that CNN is the most effective approach for cervical cell classification in this study. However, limitations such as class imbalance and occasional segmentation inconsistencies impacted overall performance, particularly in detecting abnormal cells. Future work will focus on improving classification accuracy especially for abnormal samples by exploring data augmentation techniques such as Generative Adversarial Networks (GANs) and implementing ensemble learning strategies. Additionally, integrating the proposed system into a real-time diagnostic platform using a graphical user interface (GUI) could support clinical decision-making and enhance cervical cancer screening programs.

Background and Objective: Cervical cancer remains one of the leading causes of death among women worldwide, particularly in regions with limited access to early screening. Pap smear screening is the primary tool for early detection, but manual interpretation is labor-intensive, subjective, and prone to inconsistency and misdiagnosis. Accurate segmentation of cervical cell nuclei is essential for automated analysis but is often hampered by overlapping cells, poor contrast, and staining variability. This research aims to develop an improved algorithm for accurate cervical nucleus segmentation to support automated Pap smear analysis. Method: The proposed method involves a combination of adaptive gamma correction for contrast enhancement, followed by Otsu thresholding for segmentation. Post-processing is performed using adaptive morphological operations to refine the results. The system is evaluated using standard image quality assessment metrics and validated against ground truth annotations. Result: The results show a significant improvement in segmentation performance over conventional methods. The proposed algorithm achieved a Precision of 0.9965, an F-measure of 97.29%, and an Accuracy of 98.39%. The PSNR value of 16.62 indicates enhanced image clarity after preprocessing. The method also improved sensitivity, leading to better identification of nuclei boundaries. Advanced preprocessing techniques, including edge-preserving filters and multi-Otsu thresholding, contributed to more accurate cell separation. The segmentation method proved effective across varying cell overlaps and staining conditions. Comparative evaluations with traditional clustering methods confirmed its superior performance. Conclusions: The proposed algorithm delivers robust and accurate segmentation of cervical cell nuclei, addressing common challenges in Pap smear image analysis. It provides a consistent framework for automated screening tools. This work enhances diagnostic reliability in cervical cancer screening and offers a foundation for broader applications in medical image analysis.