## 1 Introduction  
Cervical cancer continues to be the fourth most common malignant tumor in women worldwide, accounting for 6.6 percent of the total of all female cancers diagnosed in 2018 [1]. Every year, more than 500 thousand women are diagnosed with cervical cancer, with less than 300 thousand dying from the disease worldwide [2]. In 2020, around 604,000 women were diagnosed with cervical cancer. Nearly 90% of almost 342,000 cervical cancer deaths in 2020 occurred in low- and middle-income countries [3]. More than 85% of cervical cancer patients live in developing countries, with Africa having the most occurrences. Cancer hasa greater chance of being fatal in these countries [4]. This is due to a lack of knowledge about the disease and restricted healthcare availability. Developed countries, on the other hand, have strategies in place to permit accurate and efficient screening tools, allowing precancerous lesions to be discovered and treated at an initial phase [5]. It is widely accepted that early identification and treatment of premalignant lesions could indeed inhibit cancer progression in nearly 90% of cervical cancer patients. As a result, detecting cervical cancer in its early stages is critical. Based on detailed microscopic examination, the Pap smear test is regarded as a well-known screening tool for the detection of cervical pre-cancerous lesions or premalignant cells. Cervical cancer is diagnosed using either the standard approach or liquid-based cytology (LBC), which is strongly suggested by a clinician based on subjective clinical examination. An excellent Pap smear test report determines the level of the tumour, if there is any, and subsequently affirms the cervix cancer identification type based on The Bethesda System (TBS) [6]. Because LBC can produce a relatively clean and more homogeneous slide for microstructural analysis than the traditional techniques, it is confirmed to be a much more effective and convenient method than the standard approach [7; 8]. Pap smear cell films may be characterized into various subgroups. The most difficult aspect of identifying these cells is that several of the cell groupings appear to be identical in terms of the size of the cell nucleus appearance. A thorough examination of such cells for tumor identification is liable to specialists' expertise and cancer aetiology, leading to misdiagnosis in certain contexts and postponed treatment [9]. Consequently, professionals found routine examination is exhausting and prone to human error. To automate the classification process, a superior resolution to this issue is required. Recent developments in computer technology have made it possible for pathologists and doctors to detect and diagnose several medical tumors and diseases using computer-aided diagnostic (CAD) systems. These automated systems are composed of two groups of families. The first group is the traditional CAD models based on standard machine learning approaches. Feature extraction is a critical step in any traditional CAD model to diagnose and classify abnormalities in medical images. Nevertheless, various groups of features, such as texture features, statistical features, and shape descriptors, must be extracted for better classification of normal and infected pap smear images. Texture features of an image give statistics about the spatial arrangement of occurrences. Texture descriptors, such as grey level co-occurrence matrix (GLCM), Gabor wavelets (GW), Discrete wavelet transform (DWT), and others, can be used to extract texture features [10]. Since those techniques focus solely on a single descriptor of the photo, they could fail with various kinds of images. A method that retrieves shape-based attributes, for instance, might not be capable of extracting other descriptors in the image that includes texture information. The more recent group of CAD systems is based on modern deep learning (DL) techniques which are capable of automatically extracting deep features from medical images.