Diagnosis of cervical cancer using CNN deep learning model with transfer learning approaches

An enormous worldwide health problem, cervical cancer is defined by the uncontrolled proliferation of cervix cells that may spread to other parts of the body. Timely discovery has the ability to cure the condition, hence effective treatment of cervical cancer needs early detection. One of the most important screening tools for early detection is the inexpensive Pap smear. Clinical image processing is improved by computer-aided diagnostic (CAD) approaches, which speeds up and improves cancer diagnosis. Variation in image look, morphology, and size, as well as problems with data availability and quality, pose obstacles to deep learning approaches for cervical cancer classification. We use deep learning in two ways: first, by extracting features from pre-trained models using a variety of machine learning techniques for image classification; and second, by applying transfer learning to cervical cancer images using pre-trained models. An innovative approach is introduced here that improves classification by combining ResNet50 and VGG19 architectures. This all-encompassing plan aims to make cervical cancer screening more efficient and accessible over the world while simultaneously improving the accuracy of diagnoses. The proposed method achieved accuracy of 94.53% and F1 score of 93% using transfer learning and pretrained model (ResNet50 and VGG19), thus achieved superior classification performance by outperforming existing methods.