Automated detection and classification of cervical cancer using pap smear microscopic images
Computer aided categorization of smear images are considered as an open problem for a last few decades. Cervical cancer is the main cause of mortality among women population worldwide and more prevalent in under developed and developing countries. This can be successfully treated and cured if detected at its early phase. Cell nuclei segmentation is one of the most significant task in cervical cell image analysis which aids in identifying the different stages of cervical cancer like mild, moderate, severe and carcinoma. Nucleus is the bio-marker which assist in identifying the malignancy of the cervical cell and enormously assist in identifying the normal and abnormal cells as the appearance of the nucleus of normal cell vary in size, shape and texture as it progresses from benign to malignancy. Computerized image analysis methods are primarily of great interest as it provides significant benefit for clinicians with reliable and timely analysis of the sample. As hand-operated screening approach suffers from a high false-positive result because of human errors, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. Dedicated image analysis algorithms provide mathematical description of the region of interest which provide a great support to Pathologist for decision making.
Shanthi, P B Dr., "Automated detection and classification of cervical cancer using pap smear microscopic images" (2022). Technical Collection. 10.