Pap Smear Image Classification with Efficient Weight Regularization for Cervical Cancer Diagnosis

Pap smears, also known as pap tests, can identify abnormal cells early that develop cervical cancer, allowing for timely intervention and treatment.Even though the incidence rate is reduced in this modern era, it poses a significant risk to human life and should be taken very seriously.An accurate and rapid system for classifying pap smear images is necessary to provide appropriate therapy.Deep Neural Networks (DNNs) have garnered much interest in recent years and have shown outstanding categorization results in computer vision.An efficient Pap Smear Image Classification (PSIC) system with Efficient Weight Regularization (EWR) in DNN is presented in this study.The main problem with neural networks is that they have large weights that overfit the training data.To overcome this difficulty, the EWR approach is employed to penalize the large weights using grid search.The proposed non-invasive support system detects pap-smear images with cancerous cells.The HERLEV dataset comprises 675 digitized abnormal images, and 242 normal images are utilized for the classification task.When using the capabilities of the EWR-DNN combination, the proposed PSIC system can work at its absolute best.The concepts described in this study also provide a possible path to increase the categorization accuracy of all medical diagnoses.Results show that the PSIC system, which employs EWR approach achieves 98.9% classification accuracy, 99.3% specificity and 98.5% sensitivity using a regularization parameter of 10 -3 .The comparison study with other deep learning models such as VGG, ResNet, AlexNet and GoogleNet also shows the superior performance of the PSIC system.