Cervical Cancer Detection Using Image Processing and Machine Learning

Cervical cancer is still a public health problem, making timely and accurate diagnosis crucial to improve treatment options and decrease the rates of mortality. Here, we proposed the improvement of the accuracy of detection in cervical cancer using image processing and machine learning algorithms. The research investigates innovative methods in imaging evaluation during colposcopic inspection by segmenting, extracting, and classifying cervical tissues to distinguish normal tissues from cervical cancer tissues. Overall, incorporating machine learning models into the diagnostic pipeline holds promise for substantially enhancing the reliability and accuracy of cervical cancer screening. Recent technological advancements in imaging modalities, associated with convenient data analysis algorithms, represent unique opportunities to detect even minor stages of cancer by offering better patient management options, thereby improving public health by reducing their statistical significance to society in terms of global financial burden.