###### Abstract
The Artificial Hummingbird Algorithm has been proposed as an optimization technique and applied for solving different optimization problems, such as determining the optimal allocation of renewable distributed generators (RDGs) [22], engineering design problems [23], predicting the tribological behavior of Cu-ALO3 [24], and optimal planning of multiple-renewable-energy-integrated distribution systems [25]. However, by analyzing the performance of the AHA, we found that there is clear room for improvement during the exploitation of the search domain. Therefore, the Aquila Optimization (AO) algorithm [26] is applied to achieve this task. AO has been applied to different applications, including hyperspectral image classification [27], Cox proportional hazards [28], and semi-/fully automated segmentation of gastric polyps [29]. The medical image classification technique developed here depends on the integration of the advantages of TL and feature selection (FS) based on MH methods. The first step in this model is to split the dataset into training and testing sets. Then, the training set is used to teach the developed model, and this is performed in two stages. The first stage is the use of the TL technique to generate context-specific representations. Thus, a fine-tuned MobileNetV3 is used to extract the features. The second stage is the presentation of a new FS method for filtering the extracted features of the image and picking only the most relevant features to enhance the overall model for medical image classification. The presented FS model depends on the improvement of the performance of the AHA by using the AO algorithm. This can be done by generating a set of solutions that refer to the subset of selected features. Then, the quality of these solutions is assessed by using the error classification and the ratio of selected features as fitness values. After that, the best solution is allocated. This provides a better fitness value. Thereafter, a competition between the operators of the AHA and AO is applied to update the solutions. The next process is the assessment of the efficiency of the selected solution according to the best solution. This is carried out by removing the irrelevant features from the testing set and using performance measures to compute the efficiency of the classification process. The main difference between the method developed here and the other techniques is the integration between MobileNetV3 and the FS based on a modified version of AHA using the AO algorithm. Each of these techniques has its own strengths that lead to the improvement of the convergence rate and the quality of the selected features. This will influence the performance in classification. The contributions of this study are as follows:  
* Proposal of a novel FS method based on improving the behavior of the Artificial Hummingbird Algorithm using Aquila Optimization. This model aims to choose the most important features from each image representation to make the classification process more efficient (using a reduced set of features). * Presentation of a comprehensive experimental study of the proposed system with a comparison of the proposed method with various state-of-the-art methods by utilizing four real-world datasets. The organization of the article is as follows. An overview of current work on diagnostic imaging is given in Section 2. In Section 3, in addition to Aquila Optimization (AO) and the Artificial Hummingbird Algorithm (AHA), we provide the background of transfer learning for feature extraction. Section 4 provides a detailed presentation of the proposed system. Section 5 presents the experimental results of our image classification technique. Section 6 discusses the drawbacks of the proposed image recognition method. Lastly, in Section 7, the paper is summarized, and directions for future work are given.