Abstract:

Cervical cancer continues to pose a critical global health challenge, particularly in regions with limited resources. Timely and accurate diagnosis through automated analysis of cervical cytology images has the potential to significantly enhance patient outcomes. This study introduces an innovative methodology that integrates Dynamic Cell Morphology Augmentation (DCMA) and Squeeze-and-Excitation Enhanced Depthwise Dilated Convolution Networks (Sqe-DDConvNet) for effective segmentation and classification of cervical cells. DCMA creates synthetic variations in cellular morphology to replicate real-world diversity, thereby improving model robustness and generalization. Sqe-DDConvNet employs channel attention mechanisms and depthwise dilated convolutions to extract multi-scale features, ensuring accurate segmentation of cellular structures. The proposed framework is validated using publicly available datasets, showcasing notable advancements in segmentation precision, classification accuracy, and computational efficiency. This work aspires to deliver a scalable and efficient solution for early detection of cervical cancer, with the potential to support widespread screening initiatives globally.

Introduction:

Cervical cancer remains one of the most pressing global health concerns, particularly in low-resource regions where access to routine screening and healthcare services is limited. It is the fourth most common cancer among women worldwide, with a significant portion of cases resulting in preventable deaths due to delayed diagnosis. Early detection of cervical cancer is critical, as it drastically improves the prognosis and reduces mortality rates. Conventional screening methods, such as Pap smears and HPV testing, rely heavily on manual examination by cytopathologists, which can be time-intensive, prone to errors, and challenging to scale in underserved areas.
Recent advancements in artificial intelligence and deep learning have opened new possibilities for automating cervical cytology image analysis. Automated systems offer the potential to provide consistent, accurate, and scalable solutions for detecting abnormal cervical cells, even in resource-constrained environments. However, developing such systems requires addressing challenges such as variations in cell morphology, staining artifacts, and the presence of overlapping or obscured cells in cytology images. This project presents a novel approach that combines Dynamic Cell Morphology Augmentation (DCMA) and Squeeze-and-Excitation Enhanced Depthwise Dilated Convolution Networks (Sqe-DDConvNet) to tackle these challenges. DCMA simulates real-world cellular variations by generating synthetic morphological transformations, enhancing the model’s ability to generalize across diverse datasets. Meanwhile, Sqe-DDConvNet integrates channel attention mechanisms and depthwise dilated convolutions to effectively capture multi-scale features, enabling precise segmentation and classification of cervical cells. By leveraging these innovations, this work aims to provide a robust and efficient framework for early cervical cancer detection. The proposed system has been evaluated on publicly available datasets, demonstrating superior performance in segmentation accuracy, classification reliability, and computational efficiency, making it a promising solution for large-scale screening programs.

Objective:

To develop a robust and efficient framework for the early detection of cervical cancer by integrating Dynamic Cell Morphology Augmentation (DCMA) and Squeeze-and-Excitation Enhanced Depthwise Dilated Convolution Networks (Sqe-DDConvNet). The objective includes:
1. Enhancing model generalization by employing DCMA to synthetically replicate real-world variations in cervical cell morphology.
2. Achieving precise segmentation of cervical cytology images using Sqe-DDConvNet, which leverages channel attention mechanisms and depthwise dilated convolutions for multi-scale feature extraction.
3. Improving classification accuracy to reliably differentiate between normal, pre-cancerous, and cancerous cells.
4. Demonstrating the scalability and computational efficiency of the proposed system for potential application in large-scale cervical cancer screening programs, particularly in resource-limited settings.
5. Validating the framework on publicly available datasets to establish its effectiveness in advancing automated cervical cancer detection methodologies.