Abdela Ahmed Mossa (PhD)

Abdela Ahmed Mossa (PhD)

Çukurova Üniversitesi

H-index: 4

Asia-Turkey

Professor Information

University

Çukurova Üniversitesi

Position

___

Citations(all)

31

Citations(since 2020)

31

Cited By

6

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

0

i10Index(since 2020)

0

Email

University Profile Page

Çukurova Üniversitesi

Research & Interests List

Medical Image Analysis

Machine Learning

Deep Learning

AI

Top articles of Abdela Ahmed Mossa (PhD)

Author Correction: CPD‑CCNN: classification of pepper disease using a concatenation of convolutional neural network models

Author Correction: CPD‑CCNN: classification of pepper disease using a concatenation of convolutional neural network models - PMC Back to Top Skip to main content NIH NLM Logo Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation Search PMC Full-Text Archive Search in PMC Advanced Search User Guide Journal List Scientific Reports PMC10918181 Other Formats PDF (736K) Actions Cite Collections Share Permalink Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Journal List Scientific Reports PMC10918181 As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Logo of scirep Sci Rep. 2024; 14: 5554. Published online 2024 Mar 6. doi: 10.1038/…

Authors

Yohannes Agegnehu Bezabh,Ayodeji Olalekan Salau,Biniyam Mulugeta Abuhayi,Abdela Ahmed Mussa,Aleka Melese Ayalew

Journal

Scientific Reports

Published Date

2024

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models(vol 13, 15581, 2023)

Agricultural products are vital to the sustainability of the economies of developing countries. Most developing countries’ economies such as Ethiopia heavily rely on agriculture. On a global scale, the pepper crop is one of the most important agricultural products in terms of human food security. However, it is susceptible to a variety of diseases which include blight leaf disease, gray leaf spot, common rust, fruit rot disease, powdery mildew symptoms on pepper leaf, and other related diseases that are all common today. Currently, more than 34 different pepper diseases have been discovered, resulting in a 33% average yield loss in pepper cultivation. Conventionally, farmers detect the disease using visual observation but this has its own demerits as it is usually not accurate and usually time consuming. In the past, a number of researchers have presented various methods for classifying pepper plant disease, especially …

Authors

Yohannes Agegnhu Bezabih,Ayodeji Olalekan Salau,Biniyam Mulugeta Abuhayi,Abdela Ahmed Mussa,Aleka Melese Ayalew

Journal

Scientific Reports

Published Date

2023/9/20

Coffee disease classification using Convolutional Neural Network based on feature concatenation

Coffee is a significant global agricultural commodity, and improving its production and maintaining quality is crucial. However, coffee plants are susceptible to various diseases that can lower production and quality. Early detection and identification of these diseases are critical in overcoming these challenges. In this study, we propose a deep learning approach for the identification and classification of coffee diseases using Convolutional Neural Networks (CNNs). Our research is divided into three phases: image preprocessing, feature extraction, and classification. Gaussian filtering and data augmentation techniques were applied to enhance the robustness of the model and reduce noise. We used a CNN to extract high-level features by combining GoogLeNet-based and RESNET-based architecture, which can capture more complex and meaningful characteristics of the input images, such as shapes, objects, and …

Authors

Biniyam Mulugeta Abuhayi,Abdela Ahmed Mossa

Journal

Informatics in Medicine Unlocked

Published Date

2023/1/1

Ensemble learning of multiview CNN models for survival time prediction of braintumor patients using multimodal MRI scans

Brain tumors have been one of the most common life-threatening diseases for all mankind. There have beenhuge efforts dedicated to the development of medical imaging techniques and radiomics to diagnose tumor patients quicklyand e? iciently. One of the main aims is to ensure that preoperative overall survival time (OS) prediction is accurate. Recently, deep learning (DL) algorithms, and particularly convolutional neural networks (CNNs) achieved promisingperformances in almost all computer vision fields. CNNs demand large training datasets and high computational costs. However, curating large annotated medical datasets are difficult and resource-intensive. The performances of singlelearners are also unsatisfactory for small datasets. Thus, this study was conducted to improve the performance of CNN models on small volumetric datasets through developing a DL-based ensemble method for OS classification of brain tumorpatients using multimodal magnetic resonance images (MRI). First, we proposed multiview CNNs: OS classifiers basedon representing the 3D MRI data as a set of 2D slices along all three planes (axial, sagittal, and coronal) and processthem using 2D CNNs. Subsequently, the predicted probabilities by the multiview CNN models were fused using standardmachine learning algorithms. The proposed approach was experimentally evaluated on 163 patients obtained from theBraTS? 17 training dataset. Our best model achieved an AUC and accuracy values of 0.93 and 92.9%, respectively, onclassifying patients with brain tumors into two OS groups, outperforming current state-of-the-art results. In addition, the FLAIR MRI …

Authors

Abdela Ahmed Mossa,Ulus Cevik

Journal

Turkish Journal of Electrical Engineering and Computer Sciences

Published Date

2021

Ensemble of Deep Learning Models for Automatic Tuberculosis Diagnosis Using Chest CT Scans: Contribution to the ImageCLEF-2020 Challenges.

Tuberculosis (TB) is a bacterial infection that mainly affects the lungs. It is a potentially serious disease killing around 2 million people a year. Nevertheless, it can be cured if treated with the right antibiotics. However, manual diagnosing of TB can be difficult, and several tests are usually conducted by clinicians. Consequently, automated diagnosis of TB based on chest Computed Tomography (CT) images for rapid and accurate diagnosis are currently of great interest. Recently, deep learning algorithms, and in particular convolutional neural network (CNN), due to the ability to learn low-and high-level discriminative features directly from images in an end-to-end architecture, have been shown to be the state-of-the-art in automatic medical image analysis. In this work, we developed a deep learning model for automated TB diagnosis using an ensemble of different CNN architectures trained on 2D images sliced from volumetric chest CT scans. The CNN-based methods proposed in this study includes Multi-View and Triplanar CNN architectures using pre-trained AlexNet, VGG11, VGG19 and GoogLeNet feature extraction layers as a backend. Using five-fold cross validation, the average AUC, Accuracy, Sensitivity and Specificity of the proposed ensemble method were 0.799, 77.1, 0.57 and 0.824, respectively, for multi-label binary classification on the ImageCLEFtuberculosis 2020 training dataset of the lung-based automated CT report generation task, which is a wellbenchmarked public dataset running every year since 2017. The result shows the strength of our model trained in a small dataset with highly unbalanced label distributions, leading to 4th …

Authors

Ulus Cevik

Published Date

2020

Professor FAQs

What is Abdela Ahmed Mossa (PhD)'s h-index at Çukurova Üniversitesi?

The h-index of Abdela Ahmed Mossa (PhD) has been 4 since 2020 and 4 in total.

What are Abdela Ahmed Mossa (PhD)'s research interests?

The research interests of Abdela Ahmed Mossa (PhD) are: Medical Image Analysis, Machine Learning, Deep Learning, AI

What is Abdela Ahmed Mossa (PhD)'s total number of citations?

Abdela Ahmed Mossa (PhD) has 31 citations in total.

What are the co-authors of Abdela Ahmed Mossa (PhD)?

The co-authors of Abdela Ahmed Mossa (PhD) are Ulus Çevik, Abdulkerim Mohammed Yibre (PhD), Halit Eriş.

Co-Authors

H-index: 7
Ulus Çevik

Ulus Çevik

Çukurova Üniversitesi

H-index: 5
Abdulkerim Mohammed Yibre (PhD)

Abdulkerim Mohammed Yibre (PhD)

Selçuk Üniversitesi

H-index: 2
Halit Eriş

Halit Eriş

Çukurova Üniversitesi

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