scholarly journals Efficient Breast Cancer Classification Using Histopathological Images and a Simple VGG

2022 ◽  
Vol 29 (1) ◽  
pp. 102-114
Author(s):  
Marcelo Luis Rodrigues Filho ◽  
Omar Andres Carmona Cortes
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Cancer Classification ◽  
Severe Condition ◽  
Learning Capabilities ◽  
Histopathological Images ◽  
Deadly Disease

Breast cancer is the second most deadly disease worldwide. This severe condition led to 627,000 people dying in 2018. Thus, early detection is critical for improving the patients' lifetime or even curing them. In this context, we can appeal to Medicine 4.0, which exploits machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.

scholarly journals Efficient Breast Cancer Classification Using Histopathological Images and a Simpler VGG

2021 ◽  
Author(s):  
Marcelo Luis Rodrigues Filho ◽  
Omar Andres Carmona Cortes
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Cancer Classification ◽  
Severe Condition ◽  
Learning Capabilities ◽  
Histopathological Images ◽  
Deadly Disease

Breast cancer is the second most deadly disease worldwide. This severe condition led 627,000 people to die in 2018. Thus, early detection is critical for improving the patients' lifetime or even cure them. In this context, we can appeal to Medicine 4.0 that exploits the machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.

scholarly journals Breast Cancer Classification Using Deep Convolutional Neural Networks

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Jennifer K Chukwu ◽  
Faisal B. Sani ◽  
Aliyu S. Nuhu
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Classification ◽  
Computer Assisted ◽  
Final Decision ◽  
Deep Convolutional Neural Networks ◽  
Breast Cancer Classification ◽  
Histopathological Images

Breast cancer remains the primary causes of death for women and much effort has been depleted in the form of screening series for prevention. Given the exponential growth in the number of mammograms collected, computer-assisted diagnosis has become a necessity. Histopathological imaging is one of the methods for cancer diagnosis where Pathologists examine tissue cells under different microscopic standards but disagree on the final decision. In this context, the use of automatic image processing techniques resulting from deep learning denotes a promising avenue for assisting in the diagnosis of breast cancer. In this paper, an android software for breast cancer classification using deep learning approach based on a Convolutional Neural Network (CNN) was developed. The software aims to classify the breast tumors to benign or malignant. Experimental results on histopathological images using the BreakHis dataset shows that the DenseNet CNN model achieved high processing performances with 96% of accuracy in the breast cancer classification task when compared with state-of-the-art modelsKeywords— Breast cancer classification, Convolutional Neural Network (CNN), deep learning, DenseNet, histopathological images  

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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