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Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets
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Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article 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. This study employs 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 an ant colony optimization (ACO) algorithm. 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 approach is evaluated 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.

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Publication Date
Tue Sep 01 2020
Journal Name
Microprocessors And Microsystems
Design considerations for a microprocessor-based Doppler radar
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Publication Date
Sun Jan 22 2023
Journal Name
Mesopotamian Journal Of Big Data
Parallel Machine Learning Algorithms
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 To expedite the learning process, a group of algorithms known as parallel machine learning algorithmscan be executed simultaneously on several computers or processors. As data grows in both size andcomplexity, and as businesses seek efficient ways to mine that data for insights, algorithms like thesewill become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are justsome of the methods described in this article for speeding up machine learning algorithms. We alsocover the benefits and threats associated with parallel machine learning, such as data splitting,communication, and scalability. We compare how well various methods perform on a variety ofmachine learning tasks and datasets, and we talk abo

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Publication Date
Sun Mar 02 2008
Journal Name
Baghdad Science Journal
Tamper Detection in Color Image
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In this work a fragile watermarking scheme is presented. This scheme is applied to digital color images in spatial domain. The image is divided into blocks, and each block has its authentication mark embedded in it, we would be able to insure which parts of the image are authentic and which parts have been modified. This authentication carries out without need to exist the original image. The results show the quality of the watermarked image is remaining very good and the watermark survived some type of unintended modification such as familiar compression software like WINRAR and ZIP

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Publication Date
Mon Sep 30 2019
Journal Name
College Of Islamic Sciences
Visual image in Farzdaq hair
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The research shows that the visual image plays an important role when Farzdaq in the issue of aesthetic perception, it enables him to feel a sense of artistic and mental perception to raise astonishment and admiration through his ability to link the optics through the suggestive image to carry us to a new vision imagined full of visual images.

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Publication Date
Mon Apr 15 2019
Journal Name
Proceedings Of The International Conference On Information And Communication Technology
Orthogonal polynomial embedded image kernel
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Publication Date
Fri May 29 2020
Journal Name
International Journal Of Psychosocial Rehabilitation
Image Fusion Techniques: A Review
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Image Fusion is being used to gather important data from such an input image array and to place it in a single output picture to make it much more meaningful & usable than either of the input images. Image fusion boosts the quality and application of data. The accuracy of the image that has fused depending on the application. It is widely used in smart robotics, audio camera fusion, photonics, system control and output, construction and inspection of electronic circuits, complex computer, software diagnostics, also smart line assembling robots. In this paper provides a literature review of different image fusion techniques in the spatial domain and frequency domain, such as averaging, min-max, block substitution, Intensity-Hue-Saturation(IH

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Publication Date
Sun Jan 01 2023
Journal Name
2nd International Conference On Mathematical Techniques And Applications: Icmta2021
Polynomial image compression: A review
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Publication Date
Wed Oct 04 2023
Journal Name
Exergy - New Technologies And Applications
High Synthetic Image Coding System
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Compressing an image and reconstructing it without degrading its original quality is one of the challenges that still exist now a day. A coding system that considers both quality and compression rate is implemented in this work. The implemented system applies a high synthetic entropy coding schema to store the compressed image at the smallest size as possible without affecting its original quality. This coding schema is applied with two transform-based techniques, one with Discrete Cosine Transform and the other with Discrete Wavelet Transform. The implemented system was tested with different standard color images and the obtained results with different evaluation metrics have been shown. A comparison was made with some previous rel

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Publication Date
Tue May 01 2018
Journal Name
Applied Surface Science
Structural, morphological, and optical characterizations of Mo, CrN and Mo:CrN sputtered coatings for potential solar selective applications
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Publication Date
Fri Dec 31 2021
Journal Name
Iraqi Geological Journal
Development of 1D-Synthetic Geomechanical Well Logs for Applications Related to Reservoir Geomechanics in Buzurgan Oil Field
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Knowledge of the distribution of the rock mechanical properties along the depth of the wells is an important task for many applications related to reservoir geomechanics. Such these applications are wellbore stability analysis, hydraulic fracturing, reservoir compaction and subsidence, sand production, and fault reactivation. A major challenge with determining the rock mechanical properties is that they are not directly measured at the wellbore. They can be only sampled at well location using rock testing. Furthermore, the core analysis provides discrete data measurements for specific depth as well as it is often available only for a few wells in a field of interest. This study presents a methodology to generate synthetic-geomechani

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