Deep Learning Based Cervical Cancer Detection Using Hunter Prey Optimizer Algorithm

Deep learning (DL) is a widely employed technique in many different sectors these days, including medicine. Many problems that may be difficult to solve with traditional synthetic intelligence (AI) strategies can be solved with DL strategies. With more than seven hundred deaths each day, cervical cancer (CC) is the second leading cause of death in women after cancer. By 2030, this number is projected to reach 400,000 each year. Nonetheless, cancer is entirely treatable if it is discovered in its early and precancerous phases. The two most well-known screening strategies for identifying cervical cancer are the Pap smear and colposcopy. but the manual screening technique has a high error rate due to human error. In order to get around this obstacle, machine learning. Nowadays, deep studying (DL) is a method this is broadly used in many other fields, consisting of medication. DL techniques may handle a wide range of problems that are difficult to solve with traditional artificial intelligence (AI) methods. Cervical cancer (CC) is the second most common cause of death for women, after breast cancer, accounting for about 700 deaths every day. This number is expected to increase to 400,000 annually by 2030. However, if cancer is detected in its early and precancerous stages, it is completely curable. Pap tests and colposcopies are the two most widely used screening methods for the detection of cervical cancer. However, due to human error, the manual screening process has a high false rate win over this project, machine learning. The proposed model achieved 97.8% ACC, 95.4% Spec, 95.7% Recall, 98.2% Prec, and 97.6% F1-Score.