Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization DOI Creative Commons

Venkata Nagaraju Thatha,

M. Karthik,

Gaddam Venu Gopal

и другие.

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

Опубликована: Май 30, 2025

Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet Gated Recurrent Unit (GRU) networks, optimized using the Hippopotamus Optimization Algorithm (HOA). Initially, DenseNet-41 extracts intricate spatial features from images. These are then processed by hybrid AlexNet-GRU model, leveraging AlexNet's robust feature extraction GRU's sequential capabilities. HOA is employed to fine-tune hyperparameters, ensuring optimal model performance. The proposed approach evaluated on benchmark datasets (BreakHis BACH), achieving classification accuracy of 99.60%, surpassing existing state-of-the-art models. results demonstrate efficacy with bio-inspired optimization techniques breast detection. research offers computationally efficient improving early clinical decision-making, potentially enhancing patient outcomes.

Язык: Английский

Differential evolution-driven optimized ensemble network for brain tumor detection DOI

Arash Hekmat,

Zuping Zhang, Omair Bilal

и другие.

International Journal of Machine Learning and Cybernetics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 16, 2025

Язык: Английский

Процитировано

1
Multi-level feature fusion network for kidney disease detection DOI
Saif Ur Rehman Khan

Computers in Biology and Medicine, Год журнала: 2025, Номер 191, С. 110214 - 110214

Опубликована: Апрель 14, 2025

Язык: Английский

Процитировано

0
Multi-Path Convolutional Architecture with Channel-Wise Attention for Multiclass Brain Tumor Detection in Magnetic Resonance Imaging Scans DOI Open Access
Muneeb A. Khan,

Tsagaanchuluun Sugir,

Byambaa Dorj

и другие.

Electronics, Год журнала: 2025, Номер 14(9), С. 1741 - 1741

Опубликована: Апрель 24, 2025

Accurately detecting and classifying brain tumors in magnetic resonance imaging (MRI) scans poses formidable challenges, stemming from the heterogeneous presentation of need for reliable, real-time diagnostic outputs. In this paper, we propose a novel multi-path convolutional architecture enhanced with channel-wise attention mechanisms, evaluated on comprehensive four-class tumor dataset. Specifically: (i) design parallel feature extraction strategy that captures nuanced morphologies, while refines salient characteristics; (ii) employ systematic data augmentation, yielding balanced dataset 6380 MRI to bolster model generalization; (iii) compare proposed against state-of-the-art models, demonstrating superior performance 97.52% accuracy, 97.63% precision, 97.18% recall, 98.32% specificity, an F1-score 97.36%; (iv) report inference speed 5.13 ms per scan, alongside higher memory footprint approximately 26 GB, underscoring both feasibility clinical application importance resource considerations. These findings collectively highlight framework’s potential improving automated detection workflows prompt further optimization broader deployment.

Язык: Английский

Процитировано

0
A lightweight neural network with feature-level fusion and attention mechanisms for brain tumor classification DOI
Omair Bilal, Sohaib Asif

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(6)

Опубликована: Май 10, 2025

Язык: Английский

Процитировано

0
Brain tumor diagnosis redefined: Leveraging image fusion for MRI enhancement classification DOI

Arash Hekmat,

Zuping Zhang, Saif Ur Rehman Khan

и другие.

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 109, С. 108040 - 108040

Опубликована: Май 13, 2025

Язык: Английский

Процитировано

0
Brain tumor classification on MRI images using fine-tuned deep learning models DOI

R Manjunath,

Yashaswini Gowda N,

H M Manu

и другие.

Machine learning for computational science and engineering, Год журнала: 2025, Номер 1(1)

Опубликована: Май 24, 2025

Язык: Английский

Процитировано

0
Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization DOI Creative Commons

Venkata Nagaraju Thatha,

M. Karthik,

Gaddam Venu Gopal

и другие.

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

Опубликована: Май 30, 2025

Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet Gated Recurrent Unit (GRU) networks, optimized using the Hippopotamus Optimization Algorithm (HOA). Initially, DenseNet-41 extracts intricate spatial features from images. These are then processed by hybrid AlexNet-GRU model, leveraging AlexNet's robust feature extraction GRU's sequential capabilities. HOA is employed to fine-tune hyperparameters, ensuring optimal model performance. The proposed approach evaluated on benchmark datasets (BreakHis BACH), achieving classification accuracy of 99.60%, surpassing existing state-of-the-art models. results demonstrate efficacy with bio-inspired optimization techniques breast detection. research offers computationally efficient improving early clinical decision-making, potentially enhancing patient outcomes.

Язык: Английский

Процитировано

0