An Early Detection of Ovarian Cancer and The Accurate Spreading Range in Human Body by using Deep Medical Learning Model

Current methods of ovarian cancer screening include physical examination, ultrasound, and blood tests to detect the cancer antagonist (CA-125). All of these are subject to appropriate restrictions. That is, the correct diagnosis of cancer by biopsy depends on the expertise of the doctor performing the test. In many cases, other disorders are also mistaken for ovarian cancer. Ultrasound also does not help differentiate tumors as malignant or benign. Although antibody tests are accurate, they do not help diagnose the disease in its early stages. Also, CA-125 antagonist levels may also increase due to growth or inflammation of the uterus. Therefore, even if all these methods are combined, it is not possible to detect the disease at an early stage. In this paper an innovation model to identify the ovarian cancer and its spreading range by using the deep medical learning model. Ovarian cancer often goes undiagnosed until it spreads to the pelvis and abdomen. But if these are detected early, these malignant tumors can be surgically removed so that they do not spread to other parts of the body. When this diagnostic approach is combined with chemotherapy for cancer, patients’ quality of life and well-being improves