In this study, a new and rapid hidden resource decomposition method has been proposed to determine noisy pixels by adopting the extreme learning machines (ELM) method. The goal of this method is not only to determine noisy pixels, but also to protect critical structural information that can be used for disease diagnosis. In order to facilitate the diagnosis and also the treatment of patients in medicine, two-dimensional (2-D) images were calculated tomography (CT) which is obtained using medical imaging techniques. Utilizing a large number of CT images, promising results have been obtained from these experiments. The proposed method has shown a significant improvement on mean squared error and peak signal-to-noise ratio. The experimental results indicate that the proposed method is statistically efficient, and it has a good performance with a high learning speed. In the experiments, the results demonstrated that remarkable successive rates were obtained through the ELM method.

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Medical images are visualized by computer and processed to obtain larger, more organized, and three-dimensional (3D) images. Thus, significant images are provided. The processed data facilitate diagnosis and treatment in the medical fields. The 3D surface models of related areas are formed by using volumetric data obtained by employing medical imaging methods such as Magnetic Resonance (MR) and Computer Tomography (CT). The purpose of this study is to obtain 3D images from the two-dimensional CT slices. These slices are obtained from the existing medical imaging devices and transferred to the z space and a mesh structure is provided between them. In addition, we investigated 3D imaging techniques, visualization, basic data types, conversion into main graphical components, and imaging algorithms. At the phase of obtaining 3D images; the image processing methods such as surface and volume imaging techniques, smoothing, denoising, and segmentation were used. The complexity and efficiency properties of the imaging algorithms were investigated and image enhancement algorithms were utilized.

Malaria is a febrile illness caused by a parasite called plasmodium. This life-threatening disease is preventable and treatable if diagnosed early. The World Health Organization aims to reduce the global malaria incidence and death rates by at least 90% until 2030. This disease is diagnosed by visually analyzing red blood cells with a microscope by experienced radiologists. Therefore, this situation may be erroneous due to subjective interpretations. In this study, red blood cells were trained with deep learning–based convolutional neural networks to diagnose malaria, and thus, their deep features were obtained. These obtained features are also trained with autoencoder networks. Thus, the chi-square feature selection algorithm was used to obtain distinctive features. Finally, the unique feature set obtained is given as an introduction to machine learning algorithms, and then a unique diagnostic model is proposed. As a result, 100% accuracy rate was obtained. The results are promising for the diagnosis of malaria disease.

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Otitis Media (OM) is a type of infectious disease caused by viruses and/or bacteria in the middle ear cavity. In the current study, it was aimed to detect the eardrum region in middle ear images and thus to diagnose OM disease by using artificial intelligence methods. The Convolution Neural Networks (CNN) model and the deep features of this model and the images obtained with the otoscope device were used. In order to separate these images as Normal and Anomalous, the end-to-end VGG16 model was directly used in the first stage of the experimental work. In the second stage of the experimental study, the activation maps of the fc6 and fc7 layers consisting of 4096 features and the Fc8 layer consisting of 1000 features of the VGG16 CNN model were obtained. Then, it was given as input to Support Vector Machines (SVM). Then, the deep features obtained from all activation maps were combined and a new feature set was obtained. In the last stage, this feature set is given as an input to SVM. Thus, the effect of the VGG16 model and the features obtained from the layers of this model on the success of distinguishing images of the eardrum was investigated. As a result of the experimental study, the best performance results were obtained with the fc6 layer. The results showed that OM disease could be accurately detected by using a deep CNN architecture. The proposed deep learning-based classification system promises highly accurate results for disease detection.

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Minerals are inorganic substances that formed homogeneously by the combination of chemicals that comprise the Earth’s crust and mines are the commercially valuable natural minerals. Since minerals form as a result of chemical interactions in different natural environments, each may have different physical properties. Manually classifying minerals with the naked eye results in distinguishing issues, economic losses, and performance problems. In this study, a deep learning–based hybrid method for distinguishing seven mineral types is proposed. Deep learning models are used to combine the features extracted from residual blocks. The most inefficient features in the feature set were chosen using metaheuristic optimization. The complement rule was then used to combine inefficient features from the feature set into clusters. Consequently, efficient features were obtained, with an overall classification accuracy of 96.14%. The use of the complement rule method in the data set, along with the optimization methods, was shown to improve classification performance. This study is expected to help improve the efficiency and speed of mineral classification.