A Deep Learning Method for Breast Cancer Classification in the Pathology Images

Liu, Min; Hu, Lanlan; Tang, Ying; Wang, Chu; He, Yu; Zeng, Chunyan; Lin, Kwei-Jay; He, Zhizi; Huo, Wujie

doi:10.1109/jbhi.2022.3187765

Cited by 75 publications

(33 citation statements)

References 35 publications

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“…They did not produce a variety of successful models. The same conclusion can be obtained from the comparison with Spanhol et al [ 11 ], Xiang et al [ 13 ], and Liu et al [ 15 ], except that Spanhol et al [ 11 ] and Liu et al [ 15 ] perform binary magnification-dependent experiments and Xiang et al [ 13 ] perform binary magnification-independent experiments. Although Bardou et al [ 12 ] have implemented different approaches to classify breast cancer in histopathological images, they have only implemented these approaches in magnification-dependent binary and multi-class classification and score lower accuracy values than the proposed model.…”

Section: Discussion Of Resultssupporting

confidence: 83%

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A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer

et al. 2022

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Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world’s leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. In this paper, a computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist’s mission. For this target, five pre-trained convolutional neural network (CNN) models are analyzed and tested—Xception, DenseNet201, InceptionResNetV2, VGG19, and ResNet152—with the help of data augmentation techniques, and a new approach is introduced for transfer learning. These models are trained and tested with histopathological images obtained from the BreakHis dataset. Multiple experiments are performed to analyze the performance of these models through carrying out magnification-dependent and magnification-independent binary and eight-class classifications. The Xception model has shown promising performance through achieving the highest classification accuracies for all the experiments. It has achieved a range of classification accuracies from 93.32% to 98.99% for magnification-independent experiments and from 90.22% to 100% for magnification-dependent experiments.

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Section: Discussion Of Resultssupporting

confidence: 83%

“…Liu et al [ 15 ] implement a CNN model called the AlexNet-BC model. This model is pre-trained on the ImageNet dataset and then fine-tuned using transfer learning.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer

et al. 2022

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“…The effectiveness of the suggested model is examined by comparison with other contemporary ML/DL algorithms, which demonstrates that the proposed AlexNet-GRU model is computationally effective. The author offers the AlexNet-BC model, a novel framework for classifying breast pathologies, relating to that of research conducted by Liu et al [ 32 ]. The ImageNet dataset is used to pre-train the model, and an enhanced dataset is used to fine-tune it.…”

Section: Related Workmentioning

confidence: 99%

MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors

Ogundokun

Misra

Akinrotimi³

et al. 2023

Sensors

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Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model “MobileNet-SVM”, which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.

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“…Therefore, the BreakHis dataset [2, 4, 9, 12, 14, 20, 23, 27-29, 31, 32, 40, 47, 48] and WBCD dataset [1,5,10,17,21,21,34,41,42] can be the most suitable dataset with a sufficient number of breast cancer images. SELF ❼ The researchers have adopted machine learning [1,6,10,24,29] or deep learning [2,4,12,14,18,28,31,40,47,48] or ensemble learning [3, 5, 17, 20, 21, 23, 27, 30, 32-34, 36, 41, 42] techniques to address the breast cancer classification problem, and put their best efforts to improve the performance of their proposed approach(s). From Table 1, we observe that most of the existing works have mainly borrowed the deep learning techniques to address the breast cancer classification problem because of image datasets [2,4,9,12,14,18,20,23,28,31,32,40,47,47,48].…”

Section: Related Workmentioning

confidence: 99%

“…SELF ❼ The researchers have adopted machine learning [1,6,10,24,29] or deep learning [2,4,12,14,18,28,31,40,47,48] or ensemble learning [3, 5, 17, 20, 21, 23, 27, 30, 32-34, 36, 41, 42] techniques to address the breast cancer classification problem, and put their best efforts to improve the performance of their proposed approach(s). From Table 1, we observe that most of the existing works have mainly borrowed the deep learning techniques to address the breast cancer classification problem because of image datasets [2,4,9,12,14,18,20,23,28,31,32,40,47,47,48]. The Convolutional Neural Network (CNN), a deep learning model, performs well on image datasets because it handles the entire feature engineering phase and extracts features from an image in an efficient way.…”

Section: Related Workmentioning

confidence: 99%

SELF: a stacked-based ensemble learning framework for breast cancer classification

Jakhar

Singh

2023

Evol. Intel.

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Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung cancer. During the past few decades, a substantial amount of cancer cases have been reported throughout the world. Among them, breast cancer has been a widely acknowledged category of cancer disease in women due to the lack of awareness. According to the world cancer survey report 2020, there are about 2.3 million cases and 685000 deaths have been reported all over the world. As, the patient-doctor ratio (PDR) is very high; consequently, there is an utmost need for a machinebased smart breast cancer diagnosis system that can detect cancer at its early stage and be cured more efficiently. The plan is to assemble scientists in both the restorative and the machine learning fields to progress toward this clinical application. This paper presents SELF, a stackedbased ensemble learning framework, to classify breast cancer at an early stage from the histopathological images of tumor cells with computeraided diagnosis tools. In this work, we use the BreakHis dataset with 7909 histopathological images obtained on 82 patients and Wisconsin Breast SELF Cancer Database (WBCD) with 569 instances for the performance evaluation. We have trained several distinct classifiers on both datasets and selected the best five common classifiers on the basis of their accuracy measure to create our ensemble model. We use the stacking ensemble technique and consider the Extra tree, Random Forest, Adaboost, Gradient Boosting, and KNN9 classifiers as the base learners, and logistic regression model as a final estimator. We have evaluated the performance of SELF on the BreakHis dataset and WBCD datasets, and achieved the testing accuracy of approximately 95% and 99% respectively. The result of the other performance parameters on BreakHis dataset also showed that our proposed framework outperforms with F1-Score, ROC, and MCC scores with values of 94.17%, 89.41%, and 80.81% respectively.

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A Deep Learning Method for Breast Cancer Classification in the Pathology Images

Cited by 75 publications

References 35 publications

A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer

A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer

MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors

SELF: a stacked-based ensemble learning framework for breast cancer classification

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