## 1 Introduction
DL techniques could be employed for various sorts of image attributes and characteristics. DL-based methods, in addition to traditional feature extraction techniques, can be used to obtain discriminatory features from raw image data. However, when it comes to classification issues, DL-based algorithms perform much better than conventional approaches [11]. Although some studies showed that coombining DL features with traditional handcrafted features could improve diagnostic performance. Among DL architectures, Convolutional neural networks (CNNs) have attained significant results in a wide range of health [12; 13] and medical imaging applications in recent years [14; 15], particularly in mammogram [16], facial images [17], histopathology [18; 19; 20; 21; 22; 23], magnetic resonance imaging [24], fundus imaging [25], computed tomography scanning [26; 27; 28]. Motivated by the CNNs' great success in several medical and health domains, they have been adopted in several CAD models for cervical cancer diagnosis. Large amounts of data are required for CNN models in order to avoid over-fitting and poor generalization. Because labeling cervical cell photos are challenging, transfer learning (TL) is intended to share the knowledge out of a source domain to a target domain in order to prevent over-fitting. CNN previously trained on ImageNet with TL can be applied to cell images [29; 30; 31]. This study proposes a CAD system based on multiple DL features and traditional handcrafted features. It employs three compact CNNs to obtain DL features from models of distinct structures. The introduced CAD also extracts statistical features, as well as textural descriptors including GLCM, DWT, and Gabor. It examines the impact of each group of features independently on diagnostic accuracy. Then it investigates the influence of fusing each DL feature set attained from every CNN with the combined handcrafted features. Finally, it merges the whole DL features with the combined handcrafted features, using principal component analysis to explore the effect of merging multiple DL features with various handcrafted features on classification accuracy. The main contributions and novelty of the proposed CAD are as follows:  
* Developing an effective CAD based on multiple compact CNNs with lower deep layers and less parameters, and several handcrafted feature extraction approaches instead of using a single methodology like the existing CADs which employs a single CNN having huge deep layers and parameters or one handcrafted approach. * The proposed CAD does not need any pre-segmentation or enhancement steps which are required by several existing CADs. * Merging features from multiple domains including spatial DL features and texture features from the time-frequency domain such as DWT and Gabor Wavelets (GW) rather than utilizing one type of feature extraction method extracted from a single domain thus improving classification accuracy. * The proposed CAD also obtains texture GLCM features as well as statistical features from the time/spatial domain. * Examining the influence of blending multiple handcrafted features with each DL feature set retrieved from every single CNN independently which is not commonly used on existing CADs. * Aggregating the multiple DL feature sets with numerous handcrafted features via a feature reduction technique such as PCA to diminish the size of the features and lower the training duration.