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Item MEDICAL DATASET CLASSIFICATION BASED ON DIFFERENT DEEP LEARNING TECHNIQUES AND META-HEURISTIC ALGORITHMS(2023-01-30) KADHIM, Yezi Ali; MISHRA, Alok; DORUK, Reşat ÖzgürMedicine is one of the fields where computer science advancement is making significant progress. The usage of Computers in Medical improves precision and accelerates data processing and diagnosis. There are currently a variety of computer assisted diagnostic systems, with deep-learning algorithms playing an important role. Systems that are more precise and faster are required. Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This study focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. In this thesis, several combinations of different deep learning techniques with the meta-heuristic algorithm, each of the deep learning methods either the convolutional neural network or the auto-encoder were applied to extract the effective features from two different medical datasets COVID-19, and the brain tumor with the aid of the meta-heuristic method to select the optimal features in order to cover a several medical datasets detections. The first combination of several pre-trained convolutional neural networks (CNN) AlexNet, GoogleNet, ResNet 50, and DenseNet 201 was used with three types of Meta-Heuristic Algorithms Ant Colony Optimization algorithm (ACO), Particle Swarm Optimization algorithm (PSO), and Genetic Algorithm (GA). The second combination was Auto-encoder with three types Meta-Heuristic Algorithms ACO, PSO, and GA which was an innovative method, that seeks to reduce the size of the dataset while maintaining the original performance of the data. The employing of deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using ACO or PSO, or GA. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed combination is evaluated with classifiers like decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), ensemble, Naive Bayes, and discriminant using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors. Also, in this thesis, a combination of different deep learning techniques with the meta-heuristic algorithm, each of the deep learning methods either the convolutional neural network or the auto-encoder were applied to extract the effective features from two different medical datasets, with the aid of the meta heuristic method to select the optimal features obtained by the particle swarm optimization algorithm (PSO) this combination is considered as an innovative method, seeks to reduce the size of the dataset while maintaining the original performance of the data. The covid-19 dataset found that the highest accuracy by the combination of CNN-PSO-SVM was 99.76%, and for the common brain tumor dataset, the accuracy of 99.51% as the highest was obtained by the combination method autoencoder-PSO KNN. We notice that the combination model of the deep learning method with the PSO feature selection algorithm takes a consuming time much longer than the same method with the ACO algorithm at the same time the accuracy of PSO is near to ACO accuracy.