Pap smear images classification based on surrounding tissues: A comparative study

• Main Authors: Alsalatie, Mohammed, Mustafa, Wan Azani, Nammneh, Lugain, Almashakbah, Feras Fares, Alquran, Hiam, Yacob, Yasmin Mohd
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Artificial neural networks; Cancer; Cervical cancer; Classifiers; Comparative studies; Image classification; Machine learning; Medical imaging; Pap smear; Principal components analysis; Resultants; Support vector machines
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0180674

Cervical cancer is one of the most known health problems faced by women around the world. Early detection of cervical cancer may reduce the mortality rate. Pap smear images are new techniques used for screening cervical cancer. This paper excludes the nucleus of pap smear images, and the resultant images are classified into seven classes based on the surrounding region nucleus. Automated features are extracted using three pre-trained convolutional neural networks (CNN). The resultant features are twenty-one. The principal component analysis reduces the dimensionality and selects the most significant features into ten features. These features are fed to two types of machine learning algorithms: support vector machine (SVM) classifier and random forest classifier. The support vector machine classifier achieved the highest accuracy for seven classes, reaching 93.1%. This method will help the physicians in the diagnosis of cervical cancer depending on the tissues, not the nucleus. Furthermore, the result can be enhanced using a huge amount of data.