Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis

Cancers, Год журнала: 2023, Номер 15(3), С. 885 - 885

Опубликована: Янв. 31, 2023

Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will helpful establishing maximal image classification performance numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based cancer (AOADL-HBCC) technique healthcare decision making. AOADL-HBCC employs noise removal based on median filtering (MF) a contrast enhancement process. In addition, the presented applies AOA SqueezeNet model to derive feature vectors. Finally, belief network (DBN) classifier Adamax hyperparameter optimizer applied order exhibit enhanced results methodology, this comparative states that displays better than other methodologies, maximum accuracy 96.77%.

Язык: Английский

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An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network

Results in Engineering, Год журнала: 2024, Номер 23, С. 102459 - 102459

Опубликована: Июнь 28, 2024

Brain tumors must be classified to determine their severity and appropriate therapy. Applying Artificial Intelligence medical imaging has enabled remarkable developments. The presented framework classifies patients with brain high accuracy efficiency using CNN, pre-trained models, the Manta Ray Foraging Optimization (MRFO) algorithm on X-ray MRI images. Additionally, CNN Transfer Learning (TL) hyperparameters will optimized through MRFO, resulting in improved performance of model. Two public datasets from Kaggle were used build models. first dataset consists two classes, while 2nd includes three contrast-enhanced T1-weighted classes. First, a patient should diagnosed as "Healthy" (or "Tumor"). When scan returns result "Healthy," no abnormalities brain. If reveals that tumor, an performed them. After that, type tumor (i.e., meningioma, pituitary, glioma) identified second proposed classifier. A comparative analysis models two-class showed VGG16's model outperformed other Besides, Xception achieved best results three-class dataset. manual review misclassifications was conducted reasons for correct evaluation suggested architecture yielded 99.96% X-rays 98.64% MRIs. deep learning most current

Язык: Английский

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Comprehensive machine and deep learning analysis of sensor-based human activity recognition

Neural Computing and Applications, Год журнала: 2023, Номер 35(17), С. 12793 - 12831

Опубликована: Март 8, 2023

Язык: Английский

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Accuracy Analysis of Deep Learning Methods in Breast Cancer Classification: A Structured Review

Diagnostics, Год журнала: 2023, Номер 13(4), С. 683 - 683

Опубликована: Фев. 11, 2023

Breast cancer is diagnosed using histopathological imaging. This task extremely time-consuming due to high image complexity and volume. However, it important facilitate the early detection of breast for medical intervention. Deep learning (DL) has become popular in imaging solutions demonstrated various levels performance diagnosing cancerous images. Nonetheless, achieving precision while minimizing overfitting remains a significant challenge classification solutions. The handling imbalanced data incorrect labeling further concern. Additional methods, such as pre-processing, ensemble, normalization techniques, have been established enhance characteristics. These methods could influence be used overcome balancing issues. Hence, developing more sophisticated DL variant improve accuracy reducing overfitting. Technological advancements fueled automated diagnosis growth recent years. paper reviewed studies on capability classify images, objective this study was systematically review analyze current research Additionally, literature from Scopus Web Science (WOS) indexes reviewed. assessed approaches applications papers published up until November 2022. findings suggest that especially convolution neural networks their hybrids, are most cutting-edge currently use. To find new technique, necessary first survey landscape existing hybrid conduct comparisons case studies.

Язык: Английский

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

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 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.

Язык: Английский

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Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging

Applied Sciences, Год журнала: 2023, Номер 13(7), С. 4255 - 4255

Опубликована: Март 27, 2023

Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect condition ensure timely treatment enhanced chances recovery. This study focuses on transfer learning with 3D U-Net models classify ductal carcinoma, most frequent subtype breast cancer, in histopathology imaging. In this research work, a dataset 162 microscopic images specimens is utilized analysis. Preprocessing original image data includes shrinking images, standardizing intensities, extracting patches size 50 × pixels. The retrieved were employed construct basic model refined that had been previously trained an extensive medical segmentation dataset. findings revealed fine-tuned (97%) outperformed simple (87%) identifying exhibited smaller loss (0.003) testing (0.041) comparison model. disparity training accuracy reveals may have overfitted indicating there room improvement. To progress computer-aided diagnosis, also adopted various augmentation methodologies. experimental approach was put forward achieved state-of-the-art performance, surpassing benchmark techniques used previous studies same field, exhibiting greater accuracy. presented scheme has promising potential better detection diagnosis practical applications mammography.

Язык: Английский

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SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Computers, materials & continua/Computers, materials & continua (Print), Год журнала: 2023, Номер 76(2), С. 2201 - 2216

Опубликована: Янв. 1, 2023

Breast cancer is a major public health concern that affects women worldwide. It leading cause of cancer-related deaths among women, and early detection crucial for successful treatment. Unfortunately, breast can often go undetected until it has reached advanced stages, making more difficult to treat. Therefore, there pressing need accurate efficient diagnostic tools detect at an stage. The proposed approach utilizes SqueezeNet with fire modules complex bypass extract informative features from mammography images. extracted are then utilized train support vector machine (SVM) image classification. SqueezeNet-guided SVM model, known as SNSVM, achieved promising results, accuracy 94.10% sensitivity 94.30%. A 10-fold cross-validation was performed ensure the robustness mean standard deviation various performance indicators were calculated across multiple runs. This model also outperforms state-of-the-art models in all indicators, indicating its superior performance. demonstrates effectiveness diagnosis using makes tool diagnosis. may have significant implications reducing mortality rates.

Язык: Английский

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IBMRFO: Improved binary manta ray foraging optimization with chaotic tent map and adaptive somersault factor for feature selection

Expert Systems with Applications, Год журнала: 2024, Номер 251, С. 123977 - 123977

Опубликована: Апрель 16, 2024

Язык: Английский

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Enhanced object detection in remote sensing images by applying metaheuristic and hybrid metaheuristic optimizers to YOLOv7 and YOLOv8

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 28, 2025

Abstract Developments in object detection algorithms are critical for urban planning, environmental monitoring, surveillance, and many other applications. The primary objective of the article was to improve precision model efficiency. paper compared performance six different metaheuristic optimization including Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Remora (ROA), Aquila (AO), Hybrid PSO–GWO (HPSGWO) combined with YOLOv7 YOLOv8. study included two distinct remote sensing datasets, RSOD VHR-10. Many measures as precision, recall, mean average (mAP) were used during training, validation, testing processes, well fit score. results show significant improvements both YOLO variants following using these strategies. GWO-optimized 0.96 mAP 50, 0.69 50:95, HPSGWO-optimized YOLOv8 0.97 0.72 50:95 had best dataset. Similarly, versions on VHR-10 dataset 0.87 0.58 0.99 YOLOv8, indicating greater performance. findings supported usefulness increasing recall rates demonstrated major significance improving recognition tasks imaging, opening up a viable route applications variety disciplines.

Язык: Английский

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A variate brain tumor segmentation, optimization, and recognition framework

Artificial Intelligence Review, Год журнала: 2022, Номер 56(7), С. 7403 - 7456

Опубликована: Дек. 15, 2022

Язык: Английский

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