###### Abstract  
This paper presents a system for medical image diagnosis that uses transfer learning (TL) and feature selection techniques. The main aim of TL on pre-trained models such as MobileNetV3 is to extract features from raw images. Here, a novel feature selection optimization algorithm called the Artificial Hummingbird Algorithm based on Aquila Optimization (AHA-AO) is proposed. The AHA-AO is used to select only the most relevant features and ensure the improvement of the overall model classification. Our methodology was evaluated using four datasets, namely, ISIC-2016, PH2, Chest-XRay, and Blood-Cell. We compared the proposed feature selection algorithm with five of the most popular feature selection optimization algorithms. We obtained an accuracy of 87.30% for the ISIC-2016 dataset, 97.50% for the PH2 dataset, 86.90% for the Chest-XRay dataset, and 88.60% for the Blood-cell dataset. The AHA-AO outperformed the other optimization techniques. Moreover, the developed AHA-AO was faster than the other feature selection models during the process of determining the relevant features. The proposed feature selection algorithm successfully improved the performance and the speed of the overall deep learning models. medical image classification; MobileNet; feature selection algorithms; Aquila Optimization; Artificial Hummingbird Algorithm +
Footnote †: journal: Medical Image Classification  
Experimentally, TL has become the go-to methodology for training image classification models [14; 15]. These models are used to extract features from datasets and then use those features to improve the prediction process. However, there are irrelevant and noisy features that can lead to the degradation of the prediction quality. Therefore, feature selection techniques can be used to tackle the process. This can be achieved by removing these irrelevant features. This can lead to the improvement of medical image recognition models [16], computational costs [4], and parameterization [17]. Moreover, feature selection based on meta-heuristic (MH) optimization techniques has been proven to be effective in addressing a wide variety of real-world applications. Instead of a single solution in traditional optimization, a set of potential solutions enables them to effectively explore the optimal solution. Various MH methodologies have been proposed for computer-aided diagnosis [18]. To minimize the number of parameters (weights) in a convolutional neural network (CNN), [PERSON] et al. [19] proposed a technique for multidimensional path building to detect breast cancer. They utilized two techniques: transfer learning and recognizing characteristics. CNNs pre-trained with significant lesions were used. This was followed by a random forest classifier, and they used a genetic algorithm (GA) with random selection and crossover. Their study observed a significant variation in features and a decrease in parameter activities when using their proposed approach. [PERSON] et al. [20] used adaptable PSO for automated diagnosis of brain tumors by using the gray-level cooccurrence matrix (GLCM) to extract features. This model improved the image quality and eliminated noise treatment, and bone strips were performed. In general, there are several methods of FS based on MH techniques. DL models have been proposed to improve medical image classification. However, these methods suffer from some limitations that affect their performance in classification. For example, the MH technique can attract local optima, and this can lead to the degradation of the efficiency of the model through the selection of irrelevant features. This motivated us to propose an alternative medical image classification technique using the integration of TL for feature extraction and the modified Artificial Hummingbird Algorithm (AHA) [21] as a feature selection technique.