Design of an Iterative Cluster-Based Model for Detection of Brain Tumors Using Deep Transfer Learning Models DOI Creative Commons

Yenumala Sankararao,

Syed Khasim

Traitement du signal, Journal Year: 2024, Volume and Issue: 41(06), P. 2909 - 2922

Published: Dec. 31, 2024

A tumor develops when brain cells exhibit abnormal growth patterns within various body locations, characterized by irregular boundaries and shapes.Typically, these tumors rapid proliferation, increasing at a rate of approximately 1.6% per day.This cell can lead to invisible illnesses alterations in psychological behavioral functions, contributing rising trend adult mortality rates worldwide.Therefore, Brain must be detected early.Failure do so may cause deadly, incurable condition.Effective therapy improves survival if early.Magnetic Resonance Imaging (MRI) is essential for finding classifying tumors.The manual nature diagnosis classification makes it prone errors, necessitating the development automated processes improved accuracy.In light considerations, we have devised with fully way use MR images find classify tumors.Our approach encompasses three key phases: pre-processing, segmentation, classification.To detect brain, utilized MRI, employing deep transfer transformed VGG19 model.Notably, our research demonstrates superior using other pre-trained Convolutional Neural Network (CNN) models such as AlexNet VGG-16.The learning model yielded accuracy achieving 98.65% (dataset 1) 99.18% 2) different datasets.

Language: Английский

Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions DOI
Amreen Batool,

Yung-Cheol Byun

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 175, P. 108412 - 108412

Published: April 16, 2024

Language: Английский

Citations

18
Comprehensive machine and deep learning analysis of sensor-based human activity recognition DOI
Hossam Magdy Balaha, Asmaa El-Sayed Hassan

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(17), P. 12793 - 12831

Published: March 8, 2023

Language: Английский

Citations

31
A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images DOI Creative Commons

Aya A. Abd El-Khalek,

Hossam Magdy Balaha, Norah Saleh Alghamdi

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 29, 2024

Abstract The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular examinations. Age-related macular degeneration (AMD), a prevalent condition over 45, is leading cause of vision impairment the elderly. This paper presents comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. crucial for precise age-related enabling timely intervention personalized treatment strategies. We have developed novel system that extracts both local global appearance markers from images. These are obtained entire retina iso-regions aligned with optical disc. Applying weighted majority voting on best classifiers improves performance, resulting an accuracy 96.85%, sensitivity 93.72%, specificity 97.89%, precision 93.86%, F1 ROC 95.85%, balanced 95.81%, sum 95.38%. not only achieves high but also provides detailed assessment severity each retinal region. approach ensures final aligns physician’s understanding aiding them ongoing follow-up patients.

Language: Английский

Citations

14
A two-stage renal disease classification based on transfer learning with hyperparameters optimization DOI Creative Commons
Mahmoud Badawy, Abdulqader M. Almars, Hossam Magdy Balaha

et al.

Frontiers in Medicine, Journal Year: 2023, Volume and Issue: 10

Published: April 5, 2023

Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which anywhere from 1 to 15% global population and thus; considered one leading causes chronic (CKD). In addition renal cancer is tenth most prevalent type cancer, accounting for 2.5% all cancers. Artificial intelligence (AI) in medical systems can assist radiologists other healthcare professionals diagnosing different (RD) with high reliability. This study proposes an AI-based transfer learning framework detect RD at early stage. The presented on CT scans images microscopic histopathological examinations will help automatically accurately classify patients using convolutional neural network (CNN), pre-trained models, optimization algorithm images. used CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, NASNetMobile. addition, Sparrow search (SpaSA) enhance model's performance best configuration. Two datasets were used, first dataset four classes: cyst, normal, stone, tumor. case latter, there five categories within second relate severity tumor: Grade 0, 1, 2, 3, 4. DenseNet201 MobileNet four-classes compared others. Besides, SGD Nesterov parameters optimizer recommended by three while two only recommend AdaGrad AdaMax. five-class dataset, Xception best. Experimental results prove superiority proposed over state-of-the-art classification models. records accuracy 99.98% (four classes) 100% (five classes).

Language: Английский

Citations

23
Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System DOI Creative Commons
Israa Sharaby, Ahmed Alksas, Ahmed Nashat

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(3), P. 486 - 486

Published: Jan. 29, 2023

Wilms' tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of multimodal, including surgery, chemotherapy, occasionally, radiation therapy. Preoperative chemotherapy used routinely European studies select indications North American trials. The objective this study was to build a novel computer-aided prediction system preoperative response tumors. A total 63 patients (age range: 6 months-14 years) were included study, after receiving their guardians' informed consent. We incorporated contrast-enhanced computed tomography imaging extract texture, shape, functionality-based features from tumors before chemotherapy. proposed consists six steps: (i) delineate tumors' images across three contrast phases; (ii) characterize texture using first- second-order textural features; (iii) shape by applying parametric spherical harmonics model, sphericity, elongation; (iv) capture intensity changes phases describe functionality; (v) apply fusion based on extracted (vi) determine final as responsive or non-responsive via tuned support vector machine classifier. achieved an overall accuracy 95.24%, with 95.65% sensitivity 94.12% specificity. Using along integrated led superior results compared other classification models. This integrates markers learning model make early predictions about how will respond can lead personalized management plans

Language: Английский

Citations

22
A hierarchical sparrow search algorithm to solve numerical optimization and estimate parameters of carbon fiber drawing process DOI
Jiankai Xue, Bo Shen, Anqi Pan

et al.

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(S1), P. 1113 - 1148

Published: July 19, 2023

Language: Английский

Citations

12
A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD) DOI Creative Commons

Aya A. Abd El-Khalek,

Hossam Magdy Balaha, Ashraf Sewelam

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(7), P. 711 - 711

Published: July 13, 2024

The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep and computer vision, fundamentally transforming the analysis retinal images. By utilizing a wide array visual cues extracted from fundus images, sophisticated artificial intelligence models have been developed diagnose various disorders. This paper concentrates on detection Age-Related Macular Degeneration (AMD), significant condition, by offering an exhaustive examination recent learning methodologies. Additionally, it discusses potential obstacles constraints associated with implementing this technology field ophthalmology. Through systematic review, research aims assess efficacy techniques discerning AMD different modalities as they shown promise disorders diagnosis. Organized around prevalent datasets imaging techniques, initially outlines assessment criteria, image preprocessing methodologies, frameworks before conducting thorough investigation diverse approaches for detection. Drawing insights more than 30 selected studies, conclusion underscores current trajectories, major challenges, future prospects diagnosis, providing valuable resource both scholars practitioners domain.

Language: Английский

Citations

5
Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images DOI Open Access

Gouri Bompem,

Dhanalakshmi Pandluri

International journal of intelligent engineering and systems, Journal Year: 2024, Volume and Issue: 17(2), P. 39 - 49

Published: Feb. 28, 2024

The uncontrolled growth of cells in human brain can lead to the formation tumors, which occur all age people.The tumor affect nerve cells, soft tissues and blood vessels.The early detection is necessary aid doctors treating cancer patients increase their survival rate.For this various deep learning models are created discovered for efficient classification.In paper, Convolutional Neural Network proposed classification MRI images using BRATS 2019, 2020 2021 dataset.The min-max normalization used research data preprocessing fed segmentation process.The mask region-based CNN employed segmenting tumors; Followed by that, Batch applied enhance training process minimize overfitting issues.The obtained result shows that model achieves better accuracy 99.55% on 99.80% 99.29% dataset ensures accurate compared with other existing methods like 3D U-Net CapsNet + latent-dynamic condition random field (LDCRF) post-processing.

Language: Английский

Citations

4
Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI DOI Creative Commons
Hossam Magdy Balaha, Sarah M. Ayyad, Ahmed Alksas

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(6), P. 629 - 629

Published: June 19, 2024

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays crucial role in improving patient outcomes. This study introduces non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the of prostate (PCa). IVIM imaging enables differentiation water molecule diffusion within capillaries outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes two-step segmentation through use three U-Net architectures extracting tumor-containing regions interest (ROIs) from segmented images. performance CAD thoroughly evaluated, considering optimal classifier comparing diagnostic value commonly used apparent coefficient (ADC). results demonstrate combination central zone (CZ) peripheral (PZ) features Random Forest Classifier (RFC) yields best performance. achieves an accuracy 84.08% balanced 82.60%. showcases sensitivity (93.24%) reasonable specificity (71.96%), along good precision (81.48%) F1 score (86.96%). These findings highlight effectiveness accurately segmenting diagnosing PCa. represents advancement methods early PCa, showcasing potential machine learning techniques. developed solution has to revolutionize PCa diagnosis, leading improved outcomes reduced healthcare costs.

Language: Английский

Citations

4
Secure and Decentralized Collaboration in Oncology: A Blockchain Approach to Tumor Segmentation DOI
Ramin Ranjbarzadeh,

Ayse Keles,

Martin Crane

et al.

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Journal Year: 2024, Volume and Issue: unknown, P. 1681 - 1686

Published: July 2, 2024

Language: Английский

Citations

4