Classification of Breast Cancer Histopathology Images Using EfficientNet Architectures

Kajala, Aditi; Jaiswal, Sandeep

doi:10.1007/978-981-19-0619-0_55

Advances in Information Communication Technology and Computing

2022

DOI: 10.1007/978-981-19-0619-0_55

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Classification of Breast Cancer Histopathology Images Using EfficientNet Architectures

Aditi Kajala

Sandeep Jaiswal

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2024

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Cited by 2 publications

References 14 publications

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Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach

Shah,

Khan,

Abrar

et al. 2024

International Journal of Intelligent Systems

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In the global fight against breast cancer, the importance of early diagnosis is unparalleled. Early identification not only improves treatment options but also significantly improves survival rates. Our research introduces an innovative ensemble method that synergistically combines the strengths of four state‐of‐the‐art convolutional neural networks (CNNs): EfficientNet, AlexNet, ResNet, and DenseNet. These networks were chosen for their architectural advances and proven efficacy in image classification tasks, particularly in medical imaging. Each network within our ensemble is uniquely optimized: EfficientNet is fine‐tuned with customized scaling to address dataset specifics; AlexNet employs a variable dropout mechanism to reduce overfitting; ResNet benefits from learnable weighted skip connections for better gradient flow; and DenseNet uses selective connectivity to balance computational efficiency and feature extraction. This ensembling strategy combines the predictive output of multiple CNNs, each trained with an individually optimized network, to enhance the ensemble’s overall diagnostic performance. This provides higher precision and stability than any model and shows outstanding performance in the early stage of breast cancer with a precision of up to 94.6%, sensitivity of 92.4%, specificity of 96.1%, and area under the curve (AUC) of 98.0%. This ensemble framework indicated a leap in the early diagnosis of breast cancer as it is a powerful tool that combines several state‐of‐the‐art techniques, hence providing better results.

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Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach

Shah,

Khan,

Abrar

et al. 2024

International Journal of Intelligent Systems

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Virtual histopathology methods in medical imaging - a systematic review

Imran,

Shafi,

Ahmad

et al. 2024

BMC Med Imaging

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Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, often resulting in time-consuming processes and variability in diagnoses. Virtual histopathology offers a more consistent, and automated approach, employing techniques like machine learning, deep learning, and image processing to simulate staining and enhance tissue analysis. This review explores the strengths, limitations, and clinical applications of these methods, highlighting recent advancements in virtual histopathological approaches. In addition, important areas are identified for future research to improve diagnostic accuracy and efficiency in clinical settings.

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Classification of Breast Cancer Histopathology Images Using EfficientNet Architectures

Cited by 2 publications

References 14 publications

Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach

Optimizing Breast Cancer Detection With an Ensemble Deep Learning Approach

Virtual histopathology methods in medical imaging - a systematic review

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