Mohammed Ghazal

Bio: Mohammed Ghazal is an academic researcher from Abu Dhabi University. The author has contributed to research in topics: Segmentation & Autism. The author has an hindex of 20, co-authored 200 publications receiving 1374 citations. Previous affiliations of Mohammed Ghazal include Amirkabir University of Technology & Islamic Azad University.

Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network

Abstract: Early diagnosis is playing an important role in preventing progress of the Alzheimer's disease (AD). This paper proposes to improve the prediction of AD with a deep 3D Convolutional Neural Network (3D-CNN), which can show generic features capturing AD biomarkers extracted from brain images, adapt to different domain datasets, and accurately classify subjects with improved fine-tuning method. The 3D-CNN is built upon a convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans for source domain. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification in target domain. In this paper, deep supervision algorithm is used to improve the performance of already proposed 3D Adaptive CNN. Experiments on the ADNI MRI dataset without skull-stripping preprocessing have shown that the proposed 3D Deeply Supervised Adaptable CNN outperforms several proposed approaches, including 3D-CNN model, other CNN-based methods and conventional classifiers by accuracy and robustness. Abilities of the proposed network to generalize the features learnt and adapt to other domains have been validated on the CADDementia dataset.

A convolutional neural network for the screening and staging of diabetic retinopathy

Abstract: Diabetic retinopathy (DR) is a serious retinal disease and is considered as a leading cause of blindness in the world Ophthalmologists use optical coherence tomography (OCT) and fundus photography for the purpose of assessing the retinal thickness, and structure, in addition to detecting edema, hemorrhage, and scars Deep learning models are mainly used to analyze OCT or fundus images, extract unique features for each stage of DR and therefore classify images and stage the disease Throughout this paper, a deep Convolutional Neural Network (CNN) with 18 convolutional layers and 3 fully connected layers is proposed to analyze fundus images and automatically distinguish between controls (ie no DR), moderate DR (ie a combination of mild and moderate Non Proliferative DR (NPDR)) and severe DR (ie a group of severe NPDR, and Proliferative DR (PDR)) with a validation accuracy of 88%-89%, a sensitivity of 87%-89%, a specificity of 94%-95%, and a Quadratic Weighted Kappa Score of 091-092 when both 5-fold, and 10-fold cross validation methods were used respectively A prior pre-processing stage was deployed where image resizing and a class-specific data augmentation were used The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care This technology enables both early diagnosis and objective tracking of disease progression which may help optimize medical therapy to minimize vision loss

Deep Learning Role in Early Diagnosis of Prostate Cancer.

Abstract: The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen-based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient-cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.

Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study

Abstract: Nowadays, the whole world is being concerned with a major health problem, which is diabetes. A very common symptom of diabetes is the diabetic foot ulcer (DFU). The early detection of such foot complications can protect diabetic patients from any dangerous stages that develop later and may require foot amputation. This work aims at building a mobile thermal imaging system that can be used as an indicator for possible developing ulcers. The proposed system consists of a thermal camera connected to a Samsung smart phone, which is used to acquire thermal images. This thermal imaging system has a simulated temperature gradient of more than 2.2 °C, which represents the temperature difference (in the literature) than can indicate a possible development of ulcers. The acquired images are processed and segmented using basic image processing techniques. The analysis and interpretation is conducted using two techniques: Otsu thresholding technique and Point-to-Point mean difference technique. The proposed system was implemented under MATLAB Mobile platform and thermal images were analyzed and interpreted. Four testing images (feet images) were used to test this procedure; one image with any temperature variation to the feet, and three images with skin temperature increased to more than 2.2 °C introduced at different locations. With the two techniques applied during the analysis and interpretation stage, the system was successful in identifying the location of the temperature increase. This work successfully implemented a mobile thermal imaging system that includes an automated method to identify possible ulcers in diabetic patients. This may give diabetic patients the ability for a frequent self-check of possible ulcers. Although this work was implemented in simulated conditions, it provides the necessary feasibility to be further developed and tested in a clinical environment.

A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.

Abstract: This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography

Abstract: Current medical imaging technologies allow visualization of tissue anatomy in the human body at resolutions ranging from 100 micrometers to 1 millimeter. These technologies are generally not sensitive enough to detect early-stage tissue abnormalities associated with diseases such as cancer and atherosclerosis, which require micrometer-scale resolution. Here, optical coherence tomography was adapted to allow high-speed visualization of tissue in a living animal with a catheter-endoscope 1 millimeter in diameter. This method, referred to as "optical biopsy," was used to obtain cross-sectional images of the rabbit gastrointestinal and respiratory tracts at 10-micrometer resolution.

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

Enhanced Depth Imaging Optical Coherence Tomography of the Choroid in Normal Eyes

Abstract: PURPOSE To measure macular choroidal thickness in normal eyes at different points using enhanced depth imaging (EDI) optical coherence tomography (OCT) and to evaluate the association of choroidal thickness and age. DESIGN Retrospective, observational case series. METHODS EDI OCT images were obtained in patients without significant retinal or choroidal pathologic features. The images were obtained by positioning a spectral-domain OCT device close enough to the eye to acquire an inverted image. Seven sections were obtained within a 5 x 30-degree area centered at the fovea, with 100 scans averaged for each section. The choroid was measured from the outer border of the retinal pigment epithelium to the inner scleral border at 500-microm intervals of a horizontal section from 3 mm temporal to the fovea to 3 mm nasal to the fovea. Statistical analysis was performed to evaluate variations of choroidal thickness at each location and to correlate choroidal thickness and patient age. RESULTS The mean age of the 30 patients (54 eyes) was 50.4 years (range, 19 to 85 years), and 14 patients (46.7%) were female. The choroid was thickest underneath the fovea (mean, 287 microm; standard deviation, +/- 76 microm). Choroidal thickness decreased rapidly in the nasal direction and averaged 145 microm (+/- 57 microm) at 3 mm nasal to the fovea. Increasing age was correlated significantly with decreasing choroidal thickness at all points measured. Regression analysis suggested that the subfoveal choroidal thickness decreased by 15.6 microm for each decade of life. CONCLUSIONS Choroidal thickness seems to vary topographically within the posterior pole. The thickness of the choroid showed a negative correlation with age. The decrease in the thickness of the choroid may play a role in the pathophysiologic features of various age-related ocular conditions.