A hybrid lightweight breast cancer classification framework using the histopathological images

Addo, Daniel; Zhou, Shijie; Sarpong, Kwabena; Nartey, Obed T.; Abdullah, Muhammed A.; Ukwuoma, Chiagoziem C.; Al-antari, Mugahed A.

doi:10.1016/j.bbe.2023.12.003

Cited by 13 publications

(2 citation statements)

References 84 publications

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“…A self-attention-based hybrid model with a random forest classifier for effective BC classification is proposed in [8]. A lightweight hybrid model integrated with depth-wise separable, multi-head self-attention for BC classification was proposed by Zhou et al [34]. Similarly, Iqbal et al [35] presented a lightweight transformer model with feature fusion capability for BC segmentation and classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

Thwin,

Malebary,

Abulfaraj

et al. 2024

Technologies

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Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods.

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Section: Literature Reviewmentioning

confidence: 99%

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

Thwin,

Malebary,

Abulfaraj

et al. 2024

Technologies

View full text Add to dashboard Cite

show abstract

“…As a summary, the collective endeavors in breast cancer histopathology image classification underscore the significance of ensemble strategies [ 25 , 26 , 27 , 28 ], direct application of CNN architectures [ 29 , 30 , 31 ], and the fusion of transfer learning with innovative model designs [ 32 , 33 , 34 ]. Inspired by these pioneering works, our study aimed to build upon existing methodologies and enhance performance on the aforementioned datasets through innovative techniques and meticulous experimentation.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology

Balasubramanian,

Al-Heejawi,

Singh

et al. 2024

Cancers

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Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.

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Advanced feature learning and classification of microscopic breast abnormalities using a robust deep transfer learning technique

Rehman,

Mahmood,

Alamri

et al. 2024

Microscopy Res & Technique

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Breast cancer is a major health threat, with early detection crucial for improving cure and survival rates. Current systems rely on imaging technology, but digital pathology and computerized analysis can enhance accuracy, reduce false predictions, and improve medical care for breast cancer patients. The study explores the challenges in identifying benign and malignant breast cancer lesions using microscopic image datasets. It introduces a low‐dimensional multiple‐channel feature‐based method for breast cancer microscopic image recognition, overcoming limitations in feature utilization and computational complexity. The method uses RGB channels for image processing and extracts features using level co‐occurrence matrix, wavelet, Gabor, and histogram of oriented gradient. This approach aims to improve diagnostic efficiency and accuracy in breast cancer treatment. The core of our method is the SqE‐DDConvNet algorithm, which utilizes a 3 × 1 convolution kernel, SqE‐DenseNet module, bilinear interpolation, and global average pooling to enhance recognition accuracy and training efficiency. Additionally, we incorporate transfer learning with pre‐trained models, including mVVGNet16, EfficientNetV2B3, ResNet101V2, and CN2XNet, preserving spatial information and achieving higher accuracy under varying magnification conditions. The method achieves higher accuracy compared to baseline models, including texture and deep semantic features. This deep learning‐based methodology contributes to more accurate image classification and unique image recognition in breast cancer microscopic images.Research Highlights Introduces a low‐dimensional multiple‐channel feature‐based method for breast cancer microscopic image recognition. Uses RGB channels for image processing and extracts features using level co‐occurrence matrix, wavelet, Gabor, and histogram of oriented gradient. Employs the SqE‐DDConvNet algorithm for enhanced recognition accuracy and training efficiency. Transfer learning with pre‐trained models preserves spatial information and achieves higher accuracy under varying magnification conditions. Evaluates predictive efficacy of transfer learning paradigms within microscopic analysis. Utilizes CNN‐based pre‐trained algorithms to enhance network performance.

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A hybrid lightweight breast cancer classification framework using the histopathological images

Cited by 13 publications

References 84 publications

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology

Advanced feature learning and classification of microscopic breast abnormalities using a robust deep transfer learning technique

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