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

Obayya, Marwa; Maashi, Mashael; Nemri, Nadhem; Mohsen, Heba; Motwakel, Abdelwahed; Osman, Azza Elneil; Alneil, Amani A.; Alsaid, Mohamed Ibrahim

doi:10.3390/cancers15030885

Cited by 48 publications

(12 citation statements)

References 21 publications

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“…Norm (AdaMax) [34,[57][58][59] is an optimization algorithm that builds upon the principles of the Adam optimizer while addressing some of its limitations. Specifcally, AdaMax is designed to ofer more stable and efective updates by modifying the way the moments (exponential moving averages of gradients and squared gradients) are computed.…”

Section: Adamax Adaptive Moment Estimation With Infnitymentioning

confidence: 99%

A Deep Learning System for Detecting Cardiomegaly Disease Based on CXR Image

Sorour,

Wafa,

Abohany

et al. 2024

International Journal of Intelligent Systems

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The potential of technology to revolutionize healthcare is exemplified by the synergy between artificial intelligence (AI) and early detection of cardiomegaly, demonstrating the power of proactive intervention in cardiovascular health. This paper presents an innovative approach that leverages advanced AI algorithms, specifically deep learning (DL) technology, for the early detection of cardiomegaly. The methodology consists of five key steps, including data collection, image preprocessing, data augmentation, feature extraction, and classification. Utilizing chest X-ray (CXR) images from the National Institutes of Health (NIH), the study applies rigorous image preprocessing operations, including color transformation and normalization. To enhance model generalization, data augmentation is employed, paving the way for two distinct DL models, a convolutional neural network (CNN) developed from scratch and a pretrained residual network with 50 layers (ResNet50), and adapted to the problem domain. Both models are systematically evaluated with five optimizers, revealing the AdaMax optimizer’s superiority for the CNN model and AdaGrad’s efficacy for the modified ResNet50. The proposed CNN with AdaMax achieves an impressive 99.91% accuracy, outperforming recent techniques in precision, recall, and F1−score. This research underscores the transformative potential of AI in cardiovascular health diagnostics, emphasizing the significance of timely intervention.

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Section: Adamax Adaptive Moment Estimation With Infnitymentioning

confidence: 99%

A Deep Learning System for Detecting Cardiomegaly Disease Based on CXR Image

Sorour,

Wafa,

Abohany

et al. 2024

International Journal of Intelligent Systems

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“…Authors in [11] developed a new CNN structure for classifying malignant and benign BC HPIs. Authors in [12] developed the AOADL-HBCC approach for making decisions. This approach uses noise removal depending on a contrast enhancement process and median filtering.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

Gurumoorthy,

Kamarasan

2024

Eng. Technol. Appl. Sci. Res.

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In medical imaging, precise recognition of Breast Cancer (BC) is a challenge due to the complications of breast tissues. Histopathological detection is still considered the standard in BC detection. Still, the dramatic increase in workload and the complexity of histopathological image (HPI) make this task labor-intensive and dependent on the pathologist, making the advance of automated and precise HPI analysis techniques needed. Due to the automated feature extraction capability, Deep Learning (DL) methods have been effectively used in different sectors, particularly in the medical imaging sector. This study develops the future search algorithm with a DL-based breast cancer detection and classification (FSADL-BCDC) method. The FSADL-BCDC technique examines HPIs to detect and classify BC. To achieve this, the FSADL-BCDC technique implements Wiener Filtering (WF)-based preprocessing to eliminate the noise in the images. Additionally, the FSADL-BCDC uses the ResNeXt method for feature extraction with a Future Search Algorithm (FSA)-based tuning procedure. For BCDC, the FSADL-BCDC technique employs a Hybrid Convolutional Neural Network along with the Long Short-Term Memory (HCNN-LSTM) approach. Finally, the Sunflower Optimization (SFO) approach adjusts the hyperparameter values of the HCNN-LSTM. The outcomes of the FSADL-BCDC are inspected on a standard medical image dataset. Extensive relational studies highlighted the improved performance of the FSADL-BCDC approach in comparison with known methods by exhibiting an output of 96.94% and 98.69% under diverse datasets.

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“…In general, the selection of different hyperparameters has a decisive impact on ultimate training outcomes [49]. Hence, multiple ablation experiments were conduc…”

Section: Hyperparameter Calibrationmentioning

confidence: 99%

A Novel Electric Motor Fault Diagnosis by Using a Convolutional Neural Network, Normalized Thermal Images and Few-Shot Learning

Li,

Wong,

Vong

et al. 2023

Electronics

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Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting an infinite amount of motor data to train the diagnostic system is impossible. To overcome these problems, a novel automatic fault diagnosis system is proposed in this study. Data features are enhanced by a normalization module based on color bars first, as the same color in various infrared thermal images represent different temperatures. Then, the few-shot learning method is used to diagnose the faults of unseen electric motors. In the few-shot learning method, the minimum dataset size required to expand system universality is fifteen pieces, effectively solving the universality problem of artificial-to-natural data migration. The method saves a large amount of training data resources and the experimental training data collection. The accuracy of the fault diagnosis system achieved 98.9% on similar motor datasets and 91.8% on the dataset of motors that varied a lot from the training motor, which proves the high reliability and universality of the system.

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

Cited by 48 publications

References 21 publications

A Deep Learning System for Detecting Cardiomegaly Disease Based on CXR Image

A Deep Learning System for Detecting Cardiomegaly Disease Based on CXR Image

Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

A Novel Electric Motor Fault Diagnosis by Using a Convolutional Neural Network, Normalized Thermal Images and Few-Shot Learning

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