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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohamed Ibrahim Shujaa
Middle Technical University
Electrical & Electronics Engineering

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Implementing bezier surface interpolation and N.N in shape reconstruction and depth estimation of a 2D image. Mohamed Ibrahim Shujaa; Ammar Alauldeen Abdulmajeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1609-1616

Abstract

This paper considers a 2D image depth estimation of an object and reconstructed it into a 3D object image. The 2D image is defined by slices contains asset of points that are located along the object contours and within the object body. The depth of these slices are estimated using the neural network technique (N.N), where five factors (slice length, angle of incident light and illumination of some of point that located along the 2D object, namely control points)are used as inputs to the network the estimated depth of the slice are mapped into a 3D surface using the interpolation technique of the Bezier spleen surface. The experimental results showed an effective performance of the proposed approach.
Enhancement of medical images diagnosis using fuzzy convolutional neural network Huda Ali Mahdi; Mohamed Ibrahim Shujaa; Entidhar Mhawes Zghair
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5729

Abstract

Brain diseases are primarily brought on by abnormal brain cell growth, which can harm the structure of the brain and eventually result in malignant brain cancer. Major challenges exist when using a computer aided diagnosis (CAD) system for an early diagnosis that enables decisive treatment, particularly when it comes to the accurate detection of various diseases in the pictures for magnetic resonance imaging (MRI). In this study, the fuzzy convolutional neural networks (FCNN) were proposed for accurate diagnosis of brain tumors (glioma, meningioma, pituitary and non-tumor) which is implemented using Keras and TensorFlow. This approach follows three steps, training, testing, and evaluation. In training process, it builds a smart model and the structure consists of seven blocks (convolution, rectified linear unit (ReLU), batch normalization, and max pooling) then use flatten, fuzzy inferences layer, and dense layer with dropout. An international dataset with 7,022 brain tumor MRI images, was tested. The evaluation model attained a high performance with training accuracy of 99.84% and validation accuracy is 98.63% with low complexity and time is 58 s per epoch. The suggested approach performs better than the other known algorithms and may be quickly and accurately used for medical picture diagnosis.
Co-Authors Ammar Alauldeen Abdulmajeed Entidhar Mhawes Zghair Huda Ali Mahdi