Cervical Cancer Detection with a Tissue Smear and a Microscopic Image inside the Deep Learning Model of Squeeze Net
Moolya Harshatha Sadananda · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: Cervical cancer is a worldwide public health problem. Cervical cancer begins in the cervical tissues, specifically at the junction where the cervix links the lower part of the uterus that connects to the vagina. Small change in the DNA cell later they might cause the multiply growth of abnormal cell, this growth is called tumours. This are most difficult to diagnose because it starts from the cervix. It starts slowly and it risk if the infection goes long-lasting and it occurs usually people over age 30. This fungus is the type of Human Papilloma Virus (HPV), virus that passed from the one another. In past a small brush or spatula is inserted through the speculum to collect cells from the surface of the cervix. Removing abnormal cells can cause bleeding and kidney failure and microbiologist will get eye pain due to continuous work in microscope. A Pap smear test can be detected and examined in a lab. This risk can be reduced through regular screening tests and by receiving a vaccine to protect against HPV infection. If any cervical cancer found in early stage, then can be removed by the surgery, medicines, chemotherapy and radiation. Majority women are diagnosed between age 35 and 44. More than 15% occur in women over age 65. Approximately 92% of women facing cervical disease are likely to survive for nearly 5 years. Regular screening tests help reduce the occurrence of cervical disease. The output is presented as a confusion matrix. MATLAB's Squeeze Net can be utilized to classify abnormal cells by employing a deep learning model for feature extraction, training, and preprocessing the dataset through SIPaKMed. The model's performance is evaluated through training as well as testing to identify the model that is most appropriate for the given activity. This approach reduces both the time required for outcome and the value of diagnosis. With a comprehensive cervical cancer detection system, the workload of microbiologists can be significantly lessened.