A Deep Learning-Based Cervical Tumor Classification System for Telehealthcare Monitoring

A growing number of technologies have been developed for Telehealth monitoring, which has become a fully integrated part of healthcare delivery. In addition to cervical cancer classification, Telehealth services are also very popular. Remote, distant, and underserved regions can receive health care through Telehealthcare Monitoring, which combines the Internet of Medical Things (IoMT) and Information and Communications Technology (ICT). There are millions of deaths every year due to cancerous diseases, especially among women. Cancer staging is vital to the evaluation and planning of operations for women with cervical cancer. Cervical cancer tumor images can be classified using deep convolutional neural networks (DCNNs). The clinical implications of deep learning are ignored by methods that rely solely on labeled data. When cervical tumors have invaded the uterine wall, doctors determine their stage. It is more accurate and consistent with the standards of medicine to classify cervical cancer based on DCNN and to predict the findings based on densities of tumor infiltration. Clinical female applicants are invited to learn about cancer staging by using DCNN models for medical images of cervical tumors. Combining a heuristic DNN with image-based prediction facilitates the construction of prior evidence of uterine wall tumor infiltration. With neural networks that use imagery, prediction error and variance will be reduced because the previous evidence will be matched to the ground truth. Patients will be connected with healthcare providers through technology in this study to detect cervical cancer and classify it outside of conventional settings.