3PCNNB-Net: Three Parallel CNN Branches for Breast Cancer Classification Through Histopathological Images

Ibraheem, Amira Mofreh; Rahouma, Kamel H.; Hamed, Hesham F. A.

doi:10.1007/s40846-021-00620-4

Cited by 19 publications

(5 citation statements)

References 35 publications

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“… SVM classifier is used. Accuracy between 93.62 and 96.99% [ 29 ] 2021 BreakHis ( ) Three parallel CNN branches with deep resudial blocks (3PCNNB-Net) are used to extract features. The features extracted using three CNN layers are fused using average technique.…”

Section: Review Of Recent Deep Learning Research Workmentioning

confidence: 99%

Deep learning approaches for breast cancer detection in histopathology images: A review

Priya C V,

V G,

B R

et al. 2024

Cancer Biomarkers

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BACKGROUND: Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images. OBJECTIVE: To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques. METHODS: This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models. RESULTS: Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images. CONCLUSION: This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.

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Section: Review Of Recent Deep Learning Research Workmentioning

confidence: 99%

Deep learning approaches for breast cancer detection in histopathology images: A review

Priya C V,

V G,

B R

et al. 2024

Cancer Biomarkers

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“…Ibraheem et al [ 24 ] proposed a three-parallel CNN branch network (3PCNNB-Net) to classify breast cancer. The 3PCNNB-Net was separated into three steps.…”

Section: Application Of Cnn In Breast Cancermentioning

confidence: 99%

A Survey of Convolutional Neural Network in Breast Cancer

Zhu¹,

Wang²,

Zhang³

2023

Computer Modeling in Engineering &Amp; Sciences

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Problems For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. Aims A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD). Methods We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation. Conclusion Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.

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“…Some studies used deeper neural networks on the two-class problem of breast cancer histopathological images, and achieved accuracy of more than 90 % on the BreakHis dataset [ 13 , 20 , 21 ]. Furthermore, the authors built the model from the perspective of increasing network width and achieved good performance [ 22 , 23 ]. Inception-ResNet considered scaling factors between 0.1 and 0.3 to scale the entire parallel convolution module (Inception), which effectively improved the model performance [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…Several recent studies resized the images to small sizes for training, but this approach inevitably damaged the image quality [ 22 , 30 , 31 ]. There are also studies cut images into patches and use voting algorithms to calculate the prediction results of the images [ 20 , 21 , 32 , 33 ], however, since the cut patches may not contain cancer cells, this can lead to bias in the voting result.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification

Yan,

Chen,

Zhang

et al. 2024

Heliyon

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3PCNNB-Net: Three Parallel CNN Branches for Breast Cancer Classification Through Histopathological Images

Cited by 19 publications

References 35 publications

Deep learning approaches for breast cancer detection in histopathology images: A review

Deep learning approaches for breast cancer detection in histopathology images: A review

A Survey of Convolutional Neural Network in Breast Cancer

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification

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