An Effective Cervical Cancer Detection Approaches - A Comparative Analysis

The chance to preserve a life is provided by early identification of cervical cancer, which is the fourth most common malignancy among women globally. Early diagnosis can lower its frequency. However, due to a number of variables, manual screening techniques are ineffective in detecting cervical cancer. However, this leads to incorrect diagnoses and excessive therapy. Therefore, researchers suggested cervical identification using both conventional and deep learning (DL) methods. This study examines future approaches for automated cervical cancer diagnosis and reviews prior work, focusing on the DL area. This is thought to ensure accurate diagnosis and may possibly lower the incidence of cervical cancer. The comparison chart is provided among various techniques in which EL-SVM model shows higher accuracy.