Intelligent Deep Learning Framework for Cervical Cancer Detection and Multi-Class Classification

Abstract Cervical cancer represents a significant global health challenge, especially in low-resource settings like Ethiopia. In these regions, limited access to diagnostic tools and healthcare services severely exacerbates mortality rates. Early detection and accurate classification are therefore crucial for effective treatment and improved patient outcomes. This study aimed to develop a deep learning-based model for cervical cancer detection and classification. The primary objective was to enhance early diagnosis and improve patient outcomes by classifying cervical cancer into distinct stages: Normal, Type 1, Type 2, Type 3, and Type 4. The research employed advanced deep learning architectures, specifically Convolutional Neural Networks (CNN), InceptionV3, and Xception, for model development. A publicly available dataset consisting of Pap smear images was utilized. This dataset underwent preprocessing techniques, including image resizing, augmentation, noise removal, and binarization to prepare it for model training. For evaluation, the dataset was systematically split, with 80% allocated for training, 10% for validation, and 10% for testing. Experimental results demonstrated the model's superior performance in detecting and classifying cervical cancer. The proposed model achieved a notable accuracy of 92.31%. This performance surpassed that of state-of-the-art models such as VGG19 and ResNet50 in comparative evaluations. The findings highlight the potential of deep learning in automating cervical cancer diagnostics. This approach offers a scalable and cost-effective solution, particularly beneficial for resource-constrained healthcare systems. This research underscores the vital importance of leveraging such technologies to bridge existing healthcare gaps and advance global health equity.