Breast cancer detection in thermograms using a hybrid of GA and GWO based deep feature selection method

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 219, P. 119643 - 119643

Published: Feb. 2, 2023

Language: Английский

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Information gain ratio-based subfeature grouping empowers particle swarm optimization for feature selection

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 286, P. 111380 - 111380

Published: Jan. 8, 2024

Language: Английский

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Optimized Double Transformer Residual Super-resolution Network-based X-ray Images for Classification of Pneumonia Identification

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113037 - 113037

Published: Jan. 1, 2025

Language: Английский

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Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(4), P. e0281815 - e0281815

Published: April 7, 2023

We have recently been witnessing that our society is starting to heal from the impacts of COVID-19. The economic, social and cultural a pandemic cannot be ignored we should properly equipped deal with similar situations in future. Recently, Monkeypox has concerning international health community its lethal for probable pandemic. In such situations, having appropriate protocols methodologies outbreak efficiently paramount interest world. Early diagnosis treatment stand as only viable option tackle problems. To this end, paper, propose an ensemble learning-based framework detect presence virus skin lesion images. first consider three pre-trained base learners, namely Inception V3, Xception DenseNet169 fine-tune on target dataset. Further, extract probabilities these deep models feed into framework. combine outcomes, Beta function-based normalization scheme learn efficient aggregation complementary information obtained learners followed by sum rule-based ensemble. extensively evaluated publicly available dataset using five-fold cross-validation setup evaluate effectiveness. model achieves average 93.39%, 88.91%, 96.78% 92.35% accuracy, precision, recall F1 scores, respectively. supporting source codes are presented https://github.com/BihanBanerjee/MonkeyPox.

Language: Английский

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Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 34691 - 34707

Published: Jan. 1, 2024

Pneumonia is a potentially life-threatening infectious disease that typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds, or lung biopsies. Accurate diagnosis crucial wrong diagnosis, inadequate treatment lack of can cause serious consequences for patients may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts diagnosing pneumonia by assisting their decision-making process. By leveraging models, healthcare professionals enhance accuracy make informed decisions suspected having pneumonia. In this study, six models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50, Efficient-NetV2L are implemented evaluated. study also incorporates the Adam optimizer, which effectively adjusts epoch all models. trained on dataset 5856 X-ray images show 87.78%, 88.94%, 90.7%, 91.66%, 87.98%, 94.02% ResNet50 EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates highest proves its robustness detection. These findings highlight potential accurately detecting predicting based images, providing valuable support clinical improving patient treatment.

Language: Английский

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Prediction of severe thunderstorm events with ensemble deep learning and radar data

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Nov. 21, 2022

The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, most used techniques rely on video prediction deep learning which take in input time series radar reflectivity images to predict next future sequence images, from predicted rainfall quantities are extrapolated. Differently previous works, present paper proposes a method, exploiting videos frames as and lightning data realize warning machine able sound timely alarms possible severe thunderstorm events. is recast classification one characterized an high level precipitation density. From technical viewpoint, computational core approach ensemble method based recently introduced value-weighted skill scores both transforming probabilistic outcomes neural network into binary predictions assessing forecasting performance. Such particularly suitable performed over since they account evolution paying attention value forecaster. result study validated against recorded Liguria region, Italy.

Language: Английский

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Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

Journal of Bionic Engineering, Journal Year: 2022, Volume and Issue: 20(3), P. 1153 - 1174

Published: Nov. 30, 2022

Language: Английский

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Simulated annealing aided genetic algorithm for gene selection from microarray data

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 158, P. 106854 - 106854

Published: March 31, 2023

Language: Английский

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Microstructural segmentation using a union of attention guided U-Net models with different color transformed images

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: April 7, 2023

Metallographic images or often called the microstructures contain important information about metals, such as strength, toughness, ductility, corrosion resistance, which are used to choose proper materials for various engineering applications. Thus by understanding microstructures, one can determine behaviour of a component made particular metal, and predict failure that in certain conditions. Image segmentation is powerful technique determination morphological features microstructure like volume fraction, inclusion morphology, void, crystal orientations. These some key factors determining physical properties metal. Therefore, automatic micro-structure characterization using image processing useful industrial applications currently adopts deep learning-based models. In this paper, we propose metallographic method an ensemble modified U-Nets. Three U-Net models having same architecture separately fed with color transformed imaged (RGB, HSV YUV). We improvise dilated convolutions attention mechanisms get finer grained features. Then apply sum-rule-based on outcomes final prediction mask. achieve mean intersection over union (IoU) score 0.677 publicly available standard dataset, namely MetalDAM. also show proposed obtains results comparable state-of-the-art methods fewer number model parameters. The source code work be found at https://github.com/mb16biswas/attention-unet .

Language: Английский

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Deep feature selection using adaptive β-Hill Climbing aided whale optimization algorithm for lung and colon cancer detection

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 83, P. 104692 - 104692

Published: Feb. 16, 2023

Language: Английский

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